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Sample records for rank statistics multivariate

  1. Applied multivariate statistical analysis

    CERN Document Server

    Härdle, Wolfgang Karl

    2015-01-01

    Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners.  It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added.  All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior.  All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...

  2. Multivariate Statistical Process Control

    DEFF Research Database (Denmark)

    Kulahci, Murat

    2013-01-01

    As sensor and computer technology continues to improve, it becomes a normal occurrence that we confront with high dimensional data sets. As in many areas of industrial statistics, this brings forth various challenges in statistical process control (SPC) and monitoring for which the aim...... is to identify “out-of-control” state of a process using control charts in order to reduce the excessive variation caused by so-called assignable causes. In practice, the most common method of monitoring multivariate data is through a statistic akin to the Hotelling’s T2. For high dimensional data with excessive...... amount of cross correlation, practitioners are often recommended to use latent structures methods such as Principal Component Analysis to summarize the data in only a few linear combinations of the original variables that capture most of the variation in the data. Applications of these control charts...

  3. Statistical methods for ranking data

    CERN Document Server

    Alvo, Mayer

    2014-01-01

    This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data. An important aspect of nonparametric statistics is oriented towards the use of ranking data. Rank correlation is defined through the notion of distance functions and the notion of compatibility is introduced to deal with incomplete data. Ranking data are also modeled using a variety of modern tools such as CART, MCMC, EM algorithm and factor analysis. This book deals with statistical methods used for analyzing such data and provides a novel and unifying approach for hypotheses testing. The techniques described in the book are illustrated with examples and the statistical software is provided on the authors’ website.

  4. A primer of multivariate statistics

    CERN Document Server

    Harris, Richard J

    2014-01-01

    Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why

  5. Applied multivariate statistics with R

    CERN Document Server

    Zelterman, Daniel

    2015-01-01

    This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the B...

  6. Ranking multivariate GARCH models by problem dimension

    NARCIS (Netherlands)

    M. Caporin (Massimiliano); M.J. McAleer (Michael)

    2010-01-01

    textabstractIn the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. The two most widely known and used are the Scalar BEKK model of Engle and Kroner (1995) and Ding and Engle (2001), and the DCC model of Engle (2002). Some recent research has begun to

  7. Aspects of multivariate statistical theory

    CERN Document Server

    Muirhead, Robb J

    2009-01-01

    The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "". . . the wealth of material on statistics concerning the multivariate normal distribution is quite exceptional. As such it is a very useful source of information for the general statistician and a must for anyone wanting to pen

  8. Multivariate Statistical Process Control Charts: An Overview

    OpenAIRE

    Bersimis, Sotiris; Psarakis, Stelios; Panaretos, John

    2006-01-01

    In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as p...

  9. Multivariate statistical methods a primer

    CERN Document Server

    Manly, Bryan FJ

    2004-01-01

    THE MATERIAL OF MULTIVARIATE ANALYSISExamples of Multivariate DataPreview of Multivariate MethodsThe Multivariate Normal DistributionComputer ProgramsGraphical MethodsChapter SummaryReferencesMATRIX ALGEBRAThe Need for Matrix AlgebraMatrices and VectorsOperations on MatricesMatrix InversionQuadratic FormsEigenvalues and EigenvectorsVectors of Means and Covariance MatricesFurther Reading Chapter SummaryReferencesDISPLAYING MULTIVARIATE DATAThe Problem of Displaying Many Variables in Two DimensionsPlotting index VariablesThe Draftsman's PlotThe Representation of Individual Data P:ointsProfiles o

  10. A multivariate rank test for comparing mass size distributions

    KAUST Repository

    Lombard, F.

    2012-04-01

    Particle size analyses of a raw material are commonplace in the mineral processing industry. Knowledge of particle size distributions is crucial in planning milling operations to enable an optimum degree of liberation of valuable mineral phases, to minimize plant losses due to an excess of oversize or undersize material or to attain a size distribution that fits a contractual specification. The problem addressed in the present paper is how to test the equality of two or more underlying size distributions. A distinguishing feature of these size distributions is that they are not based on counts of individual particles. Rather, they are mass size distributions giving the fractions of the total mass of a sampled material lying in each of a number of size intervals. As such, the data are compositional in nature, using the terminology of Aitchison [1] that is, multivariate vectors the components of which add to 100%. In the literature, various versions of Hotelling\\'s T 2 have been used to compare matched pairs of such compositional data. In this paper, we propose a robust test procedure based on ranks as a competitor to Hotelling\\'s T 2. In contrast to the latter statistic, the power of the rank test is not unduly affected by the presence of outliers or of zeros among the data. © 2012 Copyright Taylor and Francis Group, LLC.

  11. Multivariate statistics exercises and solutions

    CERN Document Server

    Härdle, Wolfgang Karl

    2015-01-01

    The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. It contains altogether more than 250 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available in the R language. All R codes and data sets may be downloaded via the quantlet download center  www.quantlet.org or via the Springer webpage. For interactive display of low-dimensional projections of a multivariate data set, we recommend GGobi.

  12. A generalization of Friedman's rank statistic

    NARCIS (Netherlands)

    Kroon, de J.; Laan, van der P.

    1983-01-01

    In this paper a very natural generalization of the two·way analysis of variance rank statistic of FRIEDMAN is given. The general distribution-free test procedure based on this statistic for the effect of J treatments in a random block design can be applied in general two-way layouts without

  13. Multivariate statistical methods a first course

    CERN Document Server

    Marcoulides, George A

    2014-01-01

    Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple variables or measures are involved. Any analysis of more than two variables or measures can loosely be considered a multivariate statistical analysis. An introductory text for students learning multivariate statistical methods for the first time, this book keeps mathematical details to a minimum while conveying the basic principles. One of the principal strategies used throughout the book--in addition to the presentation of actual data analyses--is poin

  14. Exact distributions of two-sample rank statistics and block rank statistics using computer algebra

    NARCIS (Netherlands)

    Wiel, van de M.A.

    1998-01-01

    We derive generating functions for various rank statistics and we use computer algebra to compute the exact null distribution of these statistics. We present various techniques for reducing time and memory space used by the computations. We use the results to write Mathematica notebooks for

  15. [Rank distributions in community ecology from the statistical viewpoint].

    Science.gov (United States)

    Maksimov, V N

    2004-01-01

    Traditional statistical methods for definition of empirical functions of abundance distribution (population, biomass, production, etc.) of species in a community are applicable for processing of multivariate data contained in the above quantitative indices of the communities. In particular, evaluation of moments of distribution suffices for convolution of the data contained in a list of species and their abundance. At the same time, the species should be ranked in the list in ascending rather than descending population and the distribution models should be analyzed on the basis of the data on abundant species only.

  16. Multivariate statistical assessment of coal properties

    Czech Academy of Sciences Publication Activity Database

    Klika, Z.; Serenčíšová, J.; Kožušníková, Alena; Kolomazník, I.; Študentová, S.; Vontorová, J.

    2014-01-01

    Roč. 128, č. 128 (2014), s. 119-127 ISSN 0378-3820 R&D Projects: GA MŠk ED2.1.00/03.0082 Institutional support: RVO:68145535 Keywords : coal properties * structural,chemical and petrographical properties * multivariate statistics Subject RIV: DH - Mining, incl. Coal Mining Impact factor: 3.352, year: 2014 http://dx.doi.org/10.1016/j.fuproc.2014.06.029

  17. MIDAS: Regionally linear multivariate discriminative statistical mapping.

    Science.gov (United States)

    Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

    2018-07-01

    Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the

  18. Statistical Optimality in Multipartite Ranking and Ordinal Regression.

    Science.gov (United States)

    Uematsu, Kazuki; Lee, Yoonkyung

    2015-05-01

    Statistical optimality in multipartite ranking is investigated as an extension of bipartite ranking. We consider the optimality of ranking algorithms through minimization of the theoretical risk which combines pairwise ranking errors of ordinal categories with differential ranking costs. The extension shows that for a certain class of convex loss functions including exponential loss, the optimal ranking function can be represented as a ratio of weighted conditional probability of upper categories to lower categories, where the weights are given by the misranking costs. This result also bridges traditional ranking methods such as proportional odds model in statistics with various ranking algorithms in machine learning. Further, the analysis of multipartite ranking with different costs provides a new perspective on non-smooth list-wise ranking measures such as the discounted cumulative gain and preference learning. We illustrate our findings with simulation study and real data analysis.

  19. Robust Ranking of Multivariate GARCH Models by Problem Dimension

    NARCIS (Netherlands)

    M. Caporin (Massimiliano); M.J. McAleer (Michael)

    2012-01-01

    textabstractDuring the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. We provide an empirical comparison of alternative MGARCH models,

  20. Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation

    NARCIS (Netherlands)

    M. Caporin (Massimiliano); M.J. McAleer (Michael)

    2011-01-01

    textabstractIn the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. In this paper, we provide an empirical comparison of a set of models,

  1. Multivariate statistical analysis of wildfires in Portugal

    Science.gov (United States)

    Costa, Ricardo; Caramelo, Liliana; Pereira, Mário

    2013-04-01

    Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).

  2. Prototyping a Distributed Information Retrieval System That Uses Statistical Ranking.

    Science.gov (United States)

    Harman, Donna; And Others

    1991-01-01

    Built using a distributed architecture, this prototype distributed information retrieval system uses statistical ranking techniques to provide better service to the end user. Distributed architecture was shown to be a feasible alternative to centralized or CD-ROM information retrieval, and user testing of the ranking methodology showed both…

  3. Method for statistical data analysis of multivariate observations

    CERN Document Server

    Gnanadesikan, R

    1997-01-01

    A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of inte

  4. Multivariate ordination statistics workshop with R slides

    OpenAIRE

    Strack, Michael

    2015-01-01

    2-hour workshop given at Macquarie University Department of Biological Sciences, 4 November 2015. Workshop was an introduction to the family of techniques falling under multivariate ordination, using the R language and drawing heavily from the book "Numerical Ecology with R" by Borcard et. al (2012).

  5. Efficient nonrigid registration using ranked order statistics

    DEFF Research Database (Denmark)

    Tennakoon, Ruwan B.; Bab-Hadiashar, Alireza; de Bruijne, Marleen

    2013-01-01

    of research. In this paper we propose a fast and accurate non-rigid registration method for intra-modality volumetric images. Our approach exploits the information provided by an order statistics based segmentation method, to find the important regions for registration and use an appropriate sampling scheme......Non-rigid image registration techniques are widely used in medical imaging applications. Due to high computational complexities of these techniques, finding appropriate registration method to both reduce the computation burden and increase the registration accuracy has become an intense area...... to target those areas and reduce the registration computation time. A unique advantage of the proposed method is its ability to identify the point of diminishing returns and stop the registration process. Our experiments on registration of real lung CT images, with expert annotated landmarks, show...

  6. A cautionary note on the rank product statistic.

    Science.gov (United States)

    Koziol, James A

    2016-06-01

    The rank product method introduced by Breitling R et al. [2004, FEBS Letters 573, 83-92] has rapidly generated popularity in practical settings, in particular, detecting differential expression of genes in microarray experiments. The purpose of this note is to point out a particular property of the rank product method, namely, its differential sensitivity to over- and underexpression. It turns out that overexpression is less likely to be detected than underexpression with the rank product statistic. We have conducted both empirical and exact power studies that demonstrate this phenomenon, and summarize these findings in this note. © 2016 Federation of European Biochemical Societies.

  7. Multivariate statistics high-dimensional and large-sample approximations

    CERN Document Server

    Fujikoshi, Yasunori; Shimizu, Ryoichi

    2010-01-01

    A comprehensive examination of high-dimensional analysis of multivariate methods and their real-world applications Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of conventional statistical tools. Written by prominent researchers in the field, the book focuses on high-dimensional and large-scale approximations and details the many basic multivariate methods used to achieve high levels of accuracy. The authors begin with a fundamental presentation of the basic

  8. Synthetic environmental indicators: A conceptual approach from the multivariate statistics

    International Nuclear Information System (INIS)

    Escobar J, Luis A

    2008-01-01

    This paper presents a general description of multivariate statistical analysis and shows two methodologies: analysis of principal components and analysis of distance, DP2. Both methods use techniques of multivariate analysis to define the true dimension of data, which is useful to estimate indicators of environmental quality.

  9. Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS

    OpenAIRE

    Sumintadireja, Prihadi; Irawan, Dasapta Erwin; Rezky, Yuanno; Gio, Prana Ugiana; Agustin, Anggita

    2016-01-01

    This file is the dataset for the following paper "Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS". Authors: Prihadi Sumintadireja1, Dasapta Erwin Irawan1, Yuano Rezky2, Prana Ugiana Gio3, Anggita Agustin1

  10. Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

    Science.gov (United States)

    Glascock, M. D.; Neff, H.; Vaughn, K. J.

    2004-06-01

    The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

  11. Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

    International Nuclear Information System (INIS)

    Glascock, M. D.; Neff, H.; Vaughn, K. J.

    2004-01-01

    The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

  12. Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

    Energy Technology Data Exchange (ETDEWEB)

    Glascock, M. D.; Neff, H. [University of Missouri, Research Reactor Center (United States); Vaughn, K. J. [Pacific Lutheran University, Department of Anthropology (United States)

    2004-06-15

    The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

  13. Multivariate statistical characterization of groundwater quality in Ain ...

    African Journals Online (AJOL)

    Administrator

    depends much on the sustainability of the available water resources. Water of .... 18 wells currently in use were selected based on the preliminary field survey carried out to ... In recent times, multivariate statistical methods have been applied ...

  14. Inverted rank distributions: Macroscopic statistics, universality classes, and critical exponents

    Science.gov (United States)

    Eliazar, Iddo; Cohen, Morrel H.

    2014-01-01

    An inverted rank distribution is an infinite sequence of positive sizes ordered in a monotone increasing fashion. Interlacing together Lorenzian and oligarchic asymptotic analyses, we establish a macroscopic classification of inverted rank distributions into five “socioeconomic” universality classes: communism, socialism, criticality, feudalism, and absolute monarchy. We further establish that: (i) communism and socialism are analogous to a “disordered phase”, feudalism and absolute monarchy are analogous to an “ordered phase”, and criticality is the “phase transition” between order and disorder; (ii) the universality classes are characterized by two critical exponents, one governing the ordered phase, and the other governing the disordered phase; (iii) communism, criticality, and absolute monarchy are characterized by sharp exponent values, and are inherently deterministic; (iv) socialism is characterized by a continuous exponent range, is inherently stochastic, and is universally governed by continuous power-law statistics; (v) feudalism is characterized by a continuous exponent range, is inherently stochastic, and is universally governed by discrete exponential statistics. The results presented in this paper yield a universal macroscopic socioeconophysical perspective of inverted rank distributions.

  15. Multivariate methods and forecasting with IBM SPSS statistics

    CERN Document Server

    Aljandali, Abdulkader

    2017-01-01

    This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics such as Factor Analysis, Dis...

  16. Statistical Inference for a Class of Multivariate Negative Binomial Distributions

    DEFF Research Database (Denmark)

    Rubak, Ege H.; Møller, Jesper; McCullagh, Peter

    This paper considers statistical inference procedures for a class of models for positively correlated count variables called -permanental random fields, and which can be viewed as a family of multivariate negative binomial distributions. Their appealing probabilistic properties have earlier been...... studied in the literature, while this is the first statistical paper on -permanental random fields. The focus is on maximum likelihood estimation, maximum quasi-likelihood estimation and on maximum composite likelihood estimation based on uni- and bivariate distributions. Furthermore, new results...

  17. Multivariate statistical analysis of major and trace element data for ...

    African Journals Online (AJOL)

    Multivariate statistical analysis of major and trace element data for niobium exploration in the peralkaline granites of the anorogenic ring-complex province of Nigeria. PO Ogunleye, EC Ike, I Garba. Abstract. No Abstract Available Journal of Mining and Geology Vol.40(2) 2004: 107-117. Full Text: EMAIL FULL TEXT EMAIL ...

  18. Statistical inference for a class of multivariate negative binomial distributions

    DEFF Research Database (Denmark)

    Rubak, Ege Holger; Møller, Jesper; McCullagh, Peter

    This paper considers statistical inference procedures for a class of models for positively correlated count variables called α-permanental random fields, and which can be viewed as a family of multivariate negative binomial distributions. Their appealing probabilistic properties have earlier been...

  19. Multivariate statistical analysis of precipitation chemistry in Northwestern Spain

    International Nuclear Information System (INIS)

    Prada-Sanchez, J.M.; Garcia-Jurado, I.; Gonzalez-Manteiga, W.; Fiestras-Janeiro, M.G.; Espada-Rios, M.I.; Lucas-Dominguez, T.

    1993-01-01

    149 samples of rainwater were collected in the proximity of a power station in northwestern Spain at three rainwater monitoring stations. The resulting data are analyzed using multivariate statistical techniques. Firstly, the Principal Component Analysis shows that there are three main sources of pollution in the area (a marine source, a rural source and an acid source). The impact from pollution from these sources on the immediate environment of the stations is studied using Factorial Discriminant Analysis. 8 refs., 7 figs., 11 tabs

  20. Multivariate statistical analysis of precipitation chemistry in Northwestern Spain

    Energy Technology Data Exchange (ETDEWEB)

    Prada-Sanchez, J.M.; Garcia-Jurado, I.; Gonzalez-Manteiga, W.; Fiestras-Janeiro, M.G.; Espada-Rios, M.I.; Lucas-Dominguez, T. (University of Santiago, Santiago (Spain). Faculty of Mathematics, Dept. of Statistics and Operations Research)

    1993-07-01

    149 samples of rainwater were collected in the proximity of a power station in northwestern Spain at three rainwater monitoring stations. The resulting data are analyzed using multivariate statistical techniques. Firstly, the Principal Component Analysis shows that there are three main sources of pollution in the area (a marine source, a rural source and an acid source). The impact from pollution from these sources on the immediate environment of the stations is studied using Factorial Discriminant Analysis. 8 refs., 7 figs., 11 tabs.

  1. Multivariate statistical analysis a high-dimensional approach

    CERN Document Server

    Serdobolskii, V

    2000-01-01

    In the last few decades the accumulation of large amounts of in­ formation in numerous applications. has stimtllated an increased in­ terest in multivariate analysis. Computer technologies allow one to use multi-dimensional and multi-parametric models successfully. At the same time, an interest arose in statistical analysis with a de­ ficiency of sample data. Nevertheless, it is difficult to describe the recent state of affairs in applied multivariate methods as satisfactory. Unimprovable (dominating) statistical procedures are still unknown except for a few specific cases. The simplest problem of estimat­ ing the mean vector with minimum quadratic risk is unsolved, even for normal distributions. Commonly used standard linear multivari­ ate procedures based on the inversion of sample covariance matrices can lead to unstable results or provide no solution in dependence of data. Programs included in standard statistical packages cannot process 'multi-collinear data' and there are no theoretical recommen­ ...

  2. Statistical regularities in the rank-citation profile of scientists.

    Science.gov (United States)

    Petersen, Alexander M; Stanley, H Eugene; Succi, Sauro

    2011-01-01

    Recent science of science research shows that scientific impact measures for journals and individual articles have quantifiable regularities across both time and discipline. However, little is known about the scientific impact distribution at the scale of an individual scientist. We analyze the aggregate production and impact using the rank-citation profile c(i)(r) of 200 distinguished professors and 100 assistant professors. For the entire range of paper rank r, we fit each c(i)(r) to a common distribution function. Since two scientists with equivalent Hirsch h-index can have significantly different c(i)(r) profiles, our results demonstrate the utility of the β(i) scaling parameter in conjunction with h(i) for quantifying individual publication impact. We show that the total number of citations C(i) tallied from a scientist's N(i) papers scales as [Formula: see text]. Such statistical regularities in the input-output patterns of scientists can be used as benchmarks for theoretical models of career progress.

  3. Application of multivariate statistical techniques in microbial ecology.

    Science.gov (United States)

    Paliy, O; Shankar, V

    2016-03-01

    Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure. © 2016 John Wiley & Sons Ltd.

  4. Identification of mine waters by statistical multivariate methods

    Energy Technology Data Exchange (ETDEWEB)

    Mali, N [IGGG, Ljubljana (Slovenia)

    1992-01-01

    Three water-bearing aquifers are present in the Velenje lignite mine. The aquifer waters have differing chemical composition; a geochemical water analysis can therefore determine the source of mine water influx. Mine water samples from different locations in the mine were analyzed, the results of chemical content and of electric conductivity of mine water were statistically processed by means of MICROGAS, SPSS-X and IN STATPAC computer programs, which apply three multivariate statistical methods (discriminate, cluster and factor analysis). Reliability of calculated values was determined with the Kolmogorov and Smirnov tests. It is concluded that laboratory analysis of single water samples can produce measurement errors, but statistical processing of water sample data can identify origin and movement of mine water. 15 refs.

  5. On the degrees of freedom of reduced-rank estimators in multivariate regression.

    Science.gov (United States)

    Mukherjee, A; Chen, K; Wang, N; Zhu, J

    We study the effective degrees of freedom of a general class of reduced-rank estimators for multivariate regression in the framework of Stein's unbiased risk estimation. A finite-sample exact unbiased estimator is derived that admits a closed-form expression in terms of the thresholded singular values of the least-squares solution and hence is readily computable. The results continue to hold in the high-dimensional setting where both the predictor and the response dimensions may be larger than the sample size. The derived analytical form facilitates the investigation of theoretical properties and provides new insights into the empirical behaviour of the degrees of freedom. In particular, we examine the differences and connections between the proposed estimator and a commonly-used naive estimator. The use of the proposed estimator leads to efficient and accurate prediction risk estimation and model selection, as demonstrated by simulation studies and a data example.

  6. Multivariate statistical pattern recognition system for reactor noise analysis

    International Nuclear Information System (INIS)

    Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

    1976-01-01

    A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system

  7. Multivariate statistical pattern recognition system for reactor noise analysis

    International Nuclear Information System (INIS)

    Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

    1975-01-01

    A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system. 19 references

  8. Classification of Specialized Farms Applying Multivariate Statistical Methods

    Directory of Open Access Journals (Sweden)

    Zuzana Hloušková

    2017-01-01

    Full Text Available Classification of specialized farms applying multivariate statistical methods The paper is aimed at application of advanced multivariate statistical methods when classifying cattle breeding farming enterprises by their economic size. Advantage of the model is its ability to use a few selected indicators compared to the complex methodology of current classification model that requires knowledge of detailed structure of the herd turnover and structure of cultivated crops. Output of the paper is intended to be applied within farm structure research focused on future development of Czech agriculture. As data source, the farming enterprises database for 2014 has been used, from the FADN CZ system. The predictive model proposed exploits knowledge of actual size classes of the farms tested. Outcomes of the linear discriminatory analysis multifactor classification method have supported the chance of filing farming enterprises in the group of Small farms (98 % filed correctly, and the Large and Very Large enterprises (100 % filed correctly. The Medium Size farms have been correctly filed at 58.11 % only. Partial shortages of the process presented have been found when discriminating Medium and Small farms.

  9. Jackknife Variance Estimator for Two Sample Linear Rank Statistics

    Science.gov (United States)

    1988-11-01

    Accesion For - - ,NTIS GPA&I "TIC TAB Unann c, nc .. [d Keywords: strong consistency; linear rank test’ influence function . i , at L By S- )Distribut...reverse if necessary and identify by block number) FIELD IGROUP SUB-GROUP Strong consistency; linear rank test; influence function . 19. ABSTRACT

  10. Multivariate statistical analysis of atom probe tomography data

    International Nuclear Information System (INIS)

    Parish, Chad M.; Miller, Michael K.

    2010-01-01

    The application of spectrum imaging multivariate statistical analysis methods, specifically principal component analysis (PCA), to atom probe tomography (APT) data has been investigated. The mathematical method of analysis is described and the results for two example datasets are analyzed and presented. The first dataset is from the analysis of a PM 2000 Fe-Cr-Al-Ti steel containing two different ultrafine precipitate populations. PCA properly describes the matrix and precipitate phases in a simple and intuitive manner. A second APT example is from the analysis of an irradiated reactor pressure vessel steel. Fine, nm-scale Cu-enriched precipitates having a core-shell structure were identified and qualitatively described by PCA. Advantages, disadvantages, and future prospects for implementing these data analysis methodologies for APT datasets, particularly with regard to quantitative analysis, are also discussed.

  11. Adjustment of geochemical background by robust multivariate statistics

    Science.gov (United States)

    Zhou, D.

    1985-01-01

    Conventional analyses of exploration geochemical data assume that the background is a constant or slowly changing value, equivalent to a plane or a smoothly curved surface. However, it is better to regard the geochemical background as a rugged surface, varying with changes in geology and environment. This rugged surface can be estimated from observed geological, geochemical and environmental properties by using multivariate statistics. A method of background adjustment was developed and applied to groundwater and stream sediment reconnaissance data collected from the Hot Springs Quadrangle, South Dakota, as part of the National Uranium Resource Evaluation (NURE) program. Source-rock lithology appears to be a dominant factor controlling the chemical composition of groundwater or stream sediments. The most efficacious adjustment procedure is to regress uranium concentration on selected geochemical and environmental variables for each lithologic unit, and then to delineate anomalies by a common threshold set as a multiple of the standard deviation of the combined residuals. Robust versions of regression and RQ-mode principal components analysis techniques were used rather than ordinary techniques to guard against distortion caused by outliers Anomalies delineated by this background adjustment procedure correspond with uranium prospects much better than do anomalies delineated by conventional procedures. The procedure should be applicable to geochemical exploration at different scales for other metals. ?? 1985.

  12. Multivariate statistical tools for the radiometric features of volcanic islands

    International Nuclear Information System (INIS)

    Basile, S.; Brai, M.; Marrale, M.; Micciche, S.; Lanzo, G.; Rizzo, S.

    2009-01-01

    The Aeolian Islands represents a Quaternary volcanic arc related to the subduction of the Ionian plate beneath the Calabrian Arc. The geochemical variability of the islands has led to a broad spectrum of magma rocks. Volcanic products from calc-alkaline (CA) to calc-alkaline high in potassium (HKCA) are present throughout the Archipelago, but products belonging to shoshonitic (SHO) and potassium (KS) series characterize the southern portion of Lipari, Vulcano and Stromboli. Tectonics also plays an important role in the process of the islands differentiation. In this work, we want to review and cross-analyze the data on Lipari, Stromboli and Vulcano, collected in measurement and sampling campaigns over the last years. Chemical data were obtained by X-ray fluorescence. High resolution gamma-ray spectrometry with germanium detectors was used to measure primordial radionuclide activities. The activity of primordial radionuclides in the volcanic products of these three islands is strongly dependent on their chemism. The highest contents are found in more differentiated products (rhyolites). The CA products have lower concentrations, while the HKCA and Shoshonitic product concentrations are in between. Calculated dose rates have been correlated with the petrochemical features in order to gain further insight in evolution and differentiation of volcanic products. Ratio matching technique and multivariate statistical analyses, such as Principal Component Analysis and Minimum Spanning Tree, have been applied as an additional tool helpful to better describe the lithological affinities of the samples. (Author)

  13. Multivariate statistical modelling based on generalized linear models

    CERN Document Server

    Fahrmeir, Ludwig

    1994-01-01

    This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...

  14. Multivariate Statistical Inference of Lightning Occurrence, and Using Lightning Observations

    Science.gov (United States)

    Boccippio, Dennis

    2004-01-01

    Two classes of multivariate statistical inference using TRMM Lightning Imaging Sensor, Precipitation Radar, and Microwave Imager observation are studied, using nonlinear classification neural networks as inferential tools. The very large and globally representative data sample provided by TRMM allows both training and validation (without overfitting) of neural networks with many degrees of freedom. In the first study, the flashing / or flashing condition of storm complexes is diagnosed using radar, passive microwave and/or environmental observations as neural network inputs. The diagnostic skill of these simple lightning/no-lightning classifiers can be quite high, over land (above 80% Probability of Detection; below 20% False Alarm Rate). In the second, passive microwave and lightning observations are used to diagnose radar reflectivity vertical structure. A priori diagnosis of hydrometeor vertical structure is highly important for improved rainfall retrieval from either orbital radars (e.g., the future Global Precipitation Mission "mothership") or radiometers (e.g., operational SSM/I and future Global Precipitation Mission passive microwave constellation platforms), we explore the incremental benefit to such diagnosis provided by lightning observations.

  15. Multivariate statistical analysis - an application to lunar materials

    International Nuclear Information System (INIS)

    Deb, M.

    1978-01-01

    The compositional characteristics of clinopyroxenes and spinels - two minerals considered to be very useful in deciphering lunar history, have been studied using the multivariate statistical method of principal component analysis. The mineral-chemical data used are from certain lunar rocks and fines collected by Apollo 11, 12, 14 and 15 and Luna 16 and 20 missions, representing mainly the mare basalts and also non-mare basalts, breccia and rock fragments from the highland regions, in which a large number of these minerals have been analyzed. The correlations noted in the mineral compositions, indicating substitutional relationships, have been interpreted on the basis of available crystal-chemical and petrological informations. Compositional trends for individual specimens have been delineated and compared by producing ''principal latent vector diagrams''. The percent variance of the principal components denoted by the eigenvalues, have been evaluated in terms of the crystallization history of the samples. Some of the major petrogenetic implications of this study concern the role of early formed cumulate phases in the near-surface fractionation of mare basalts, mixing of mineral compositions in the highland regolith and the subsolidus reduction trends in lunar spinels. (auth.)

  16. Classification of Malaysia aromatic rice using multivariate statistical analysis

    International Nuclear Information System (INIS)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

    2015-01-01

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties

  17. Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

    Science.gov (United States)

    Gulgundi, Mohammad Shahid; Shetty, Amba

    2018-03-01

    Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.

  18. Classification of Malaysia aromatic rice using multivariate statistical analysis

    Energy Technology Data Exchange (ETDEWEB)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A. [School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600 Arau, Perlis (Malaysia); Omar, O. [Malaysian Agriculture Research and Development Institute (MARDI), Persiaran MARDI-UPM, 43400 Serdang, Selangor (Malaysia)

    2015-05-15

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

  19. Classification of Malaysia aromatic rice using multivariate statistical analysis

    Science.gov (United States)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

    2015-05-01

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

  20. The Inappropriate Symmetries of Multivariate Statistical Analysis in Geometric Morphometrics.

    Science.gov (United States)

    Bookstein, Fred L

    In today's geometric morphometrics the commonest multivariate statistical procedures, such as principal component analysis or regressions of Procrustes shape coordinates on Centroid Size, embody a tacit roster of symmetries -axioms concerning the homogeneity of the multiple spatial domains or descriptor vectors involved-that do not correspond to actual biological fact. These techniques are hence inappropriate for any application regarding which we have a-priori biological knowledge to the contrary (e.g., genetic/morphogenetic processes common to multiple landmarks, the range of normal in anatomy atlases, the consequences of growth or function for form). But nearly every morphometric investigation is motivated by prior insights of this sort. We therefore need new tools that explicitly incorporate these elements of knowledge, should they be quantitative, to break the symmetries of the classic morphometric approaches. Some of these are already available in our literature but deserve to be known more widely: deflated (spatially adaptive) reference distributions of Procrustes coordinates, Sewall Wright's century-old variant of factor analysis, the geometric algebra of importing explicit biomechanical formulas into Procrustes space. Other methods, not yet fully formulated, might involve parameterized models for strain in idealized forms under load, principled approaches to the separation of functional from Brownian aspects of shape variation over time, and, in general, a better understanding of how the formalism of landmarks interacts with the many other approaches to quantification of anatomy. To more powerfully organize inferences from the high-dimensional measurements that characterize so much of today's organismal biology, tomorrow's toolkit must rely neither on principal component analysis nor on the Procrustes distance formula, but instead on sound prior biological knowledge as expressed in formulas whose coefficients are not all the same. I describe the problems

  1. Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

    Science.gov (United States)

    Hohn, M. Ed; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

    1980-01-01

    Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ??-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data. ?? 1980 Plenum Publishing Corporation.

  2. Sensitivity analysis of ranked data: from order statistics to quantiles

    NARCIS (Netherlands)

    Heidergott, B.F.; Volk-Makarewicz, W.

    2015-01-01

    In this paper we provide the mathematical theory for sensitivity analysis of order statistics of continuous random variables, where the sensitivity is with respect to a distributional parameter. Sensitivity analysis of order statistics over a finite number of observations is discussed before

  3. Wilcoxon's signed-rank statistic: what null hypothesis and why it matters.

    Science.gov (United States)

    Li, Heng; Johnson, Terri

    2014-01-01

    In statistical literature, the term 'signed-rank test' (or 'Wilcoxon signed-rank test') has been used to refer to two distinct tests: a test for symmetry of distribution and a test for the median of a symmetric distribution, sharing a common test statistic. To avoid potential ambiguity, we propose to refer to those two tests by different names, as 'test for symmetry based on signed-rank statistic' and 'test for median based on signed-rank statistic', respectively. The utility of such terminological differentiation should become evident through our discussion of how those tests connect and contrast with sign test and one-sample t-test. Published 2014. This article is a U.S. Government work and is in the public domain in the USA. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

  4. An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

    International Nuclear Information System (INIS)

    Weathers, J.B.; Luck, R.; Weathers, J.W.

    2009-01-01

    The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.

  5. An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

    Energy Technology Data Exchange (ETDEWEB)

    Weathers, J.B. [Shock, Noise, and Vibration Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: James.Weathers@ngc.com; Luck, R. [Department of Mechanical Engineering, Mississippi State University, 210 Carpenter Engineering Building, P.O. Box ME, Mississippi State, MS 39762-5925 (United States)], E-mail: Luck@me.msstate.edu; Weathers, J.W. [Structural Analysis Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: Jeffrey.Weathers@ngc.com

    2009-11-15

    The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.

  6. Application of Parallel Hierarchical Matrices and Low-Rank Tensors in Spatial Statistics and Parameter Identification

    KAUST Repository

    Litvinenko, Alexander

    2018-03-12

    Part 1: Parallel H-matrices in spatial statistics 1. Motivation: improve statistical model 2. Tools: Hierarchical matrices 3. Matern covariance function and joint Gaussian likelihood 4. Identification of unknown parameters via maximizing Gaussian log-likelihood 5. Implementation with HLIBPro. Part 2: Low-rank Tucker tensor methods in spatial statistics

  7. The exact probability distribution of the rank product statistics for replicated experiments.

    Science.gov (United States)

    Eisinga, Rob; Breitling, Rainer; Heskes, Tom

    2013-03-18

    The rank product method is a widely accepted technique for detecting differentially regulated genes in replicated microarray experiments. To approximate the sampling distribution of the rank product statistic, the original publication proposed a permutation approach, whereas recently an alternative approximation based on the continuous gamma distribution was suggested. However, both approximations are imperfect for estimating small tail probabilities. In this paper we relate the rank product statistic to number theory and provide a derivation of its exact probability distribution and the true tail probabilities. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  8. Attitudes toward Advanced and Multivariate Statistics When Using Computers.

    Science.gov (United States)

    Kennedy, Robert L.; McCallister, Corliss Jean

    This study investigated the attitudes toward statistics of graduate students who studied advanced statistics in a course in which the focus of instruction was the use of a computer program in class. The use of the program made it possible to provide an individualized, self-paced, student-centered, and activity-based course. The three sections…

  9. Multivariate Location Estimation Using Extension of $R$-Estimates Through $U$-Statistics Type Approach

    OpenAIRE

    Chaudhuri, Probal

    1992-01-01

    We consider a class of $U$-statistics type estimates for multivariate location. The estimates extend some $R$-estimates to multivariate data. In particular, the class of estimates includes the multivariate median considered by Gini and Galvani (1929) and Haldane (1948) and a multivariate extension of the well-known Hodges-Lehmann (1963) estimate. We explore large sample behavior of these estimates by deriving a Bahadur type representation for them. In the process of developing these asymptoti...

  10. Multivariate statistical analysis of a multi-step industrial processes

    DEFF Research Database (Denmark)

    Reinikainen, S.P.; Høskuldsson, Agnar

    2007-01-01

    Monitoring and quality control of industrial processes often produce information on how the data have been obtained. In batch processes, for instance, the process is carried out in stages; some process or control parameters are set at each stage. However, the obtained data might not be utilized...... efficiently, even if this information may reveal significant knowledge about process dynamics or ongoing phenomena. When studying the process data, it may be important to analyse the data in the light of the physical or time-wise development of each process step. In this paper, a unified approach to analyse...... multivariate multi-step processes, where results from each step are used to evaluate future results, is presented. The methods presented are based on Priority PLS Regression. The basic idea is to compute the weights in the regression analysis for given steps, but adjust all data by the resulting score vectors...

  11. Poisson statistics of PageRank probabilities of Twitter and Wikipedia networks

    Science.gov (United States)

    Frahm, Klaus M.; Shepelyansky, Dima L.

    2014-04-01

    We use the methods of quantum chaos and Random Matrix Theory for analysis of statistical fluctuations of PageRank probabilities in directed networks. In this approach the effective energy levels are given by a logarithm of PageRank probability at a given node. After the standard energy level unfolding procedure we establish that the nearest spacing distribution of PageRank probabilities is described by the Poisson law typical for integrable quantum systems. Our studies are done for the Twitter network and three networks of Wikipedia editions in English, French and German. We argue that due to absence of level repulsion the PageRank order of nearby nodes can be easily interchanged. The obtained Poisson law implies that the nearby PageRank probabilities fluctuate as random independent variables.

  12. Finding differentially expressed genes in high dimensional data: Rank based test statistic via a distance measure.

    Science.gov (United States)

    Mathur, Sunil; Sadana, Ajit

    2015-12-01

    We present a rank-based test statistic for the identification of differentially expressed genes using a distance measure. The proposed test statistic is highly robust against extreme values and does not assume the distribution of parent population. Simulation studies show that the proposed test is more powerful than some of the commonly used methods, such as paired t-test, Wilcoxon signed rank test, and significance analysis of microarray (SAM) under certain non-normal distributions. The asymptotic distribution of the test statistic, and the p-value function are discussed. The application of proposed method is shown using a real-life data set. © The Author(s) 2011.

  13. Refining developmental coordination disorder subtyping with multivariate statistical methods

    Directory of Open Access Journals (Sweden)

    Lalanne Christophe

    2012-07-01

    Full Text Available Abstract Background With a large number of potentially relevant clinical indicators penalization and ensemble learning methods are thought to provide better predictive performance than usual linear predictors. However, little is known about how they perform in clinical studies where few cases are available. We used Random Forests and Partial Least Squares Discriminant Analysis to select the most salient impairments in Developmental Coordination Disorder (DCD and assess patients similarity. Methods We considered a wide-range testing battery for various neuropsychological and visuo-motor impairments which aimed at characterizing subtypes of DCD in a sample of 63 children. Classifiers were optimized on a training sample, and they were used subsequently to rank the 49 items according to a permuted measure of variable importance. In addition, subtyping consistency was assessed with cluster analysis on the training sample. Clustering fitness and predictive accuracy were evaluated on the validation sample. Results Both classifiers yielded a relevant subset of items impairments that altogether accounted for a sharp discrimination between three DCD subtypes: ideomotor, visual-spatial and constructional, and mixt dyspraxia. The main impairments that were found to characterize the three subtypes were: digital perception, imitations of gestures, digital praxia, lego blocks, visual spatial structuration, visual motor integration, coordination between upper and lower limbs. Classification accuracy was above 90% for all classifiers, and clustering fitness was found to be satisfactory. Conclusions Random Forests and Partial Least Squares Discriminant Analysis are useful tools to extract salient features from a large pool of correlated binary predictors, but also provide a way to assess individuals proximities in a reduced factor space. Less than 15 neuro-visual, neuro-psychomotor and neuro-psychological tests might be required to provide a sensitive and

  14. A multivariate and stochastic approach to identify key variables to rank dairy farms on profitability.

    Science.gov (United States)

    Atzori, A S; Tedeschi, L O; Cannas, A

    2013-05-01

    The economic efficiency of dairy farms is the main goal of farmers. The objective of this work was to use routinely available information at the dairy farm level to develop an index of profitability to rank dairy farms and to assist the decision-making process of farmers to increase the economic efficiency of the entire system. A stochastic modeling approach was used to study the relationships between inputs and profitability (i.e., income over feed cost; IOFC) of dairy cattle farms. The IOFC was calculated as: milk revenue + value of male calves + culling revenue - herd feed costs. Two databases were created. The first one was a development database, which was created from technical and economic variables collected in 135 dairy farms. The second one was a synthetic database (sDB) created from 5,000 synthetic dairy farms using the Monte Carlo technique and based on the characteristics of the development database data. The sDB was used to develop a ranking index as follows: (1) principal component analysis (PCA), excluding IOFC, was used to identify principal components (sPC); and (2) coefficient estimates of a multiple regression of the IOFC on the sPC were obtained. Then, the eigenvectors of the sPC were used to compute the principal component values for the original 135 dairy farms that were used with the multiple regression coefficient estimates to predict IOFC (dRI; ranking index from development database). The dRI was used to rank the original 135 dairy farms. The PCA explained 77.6% of the sDB variability and 4 sPC were selected. The sPC were associated with herd profile, milk quality and payment, poor management, and reproduction based on the significant variables of the sPC. The mean IOFC in the sDB was 0.1377 ± 0.0162 euros per liter of milk (€/L). The dRI explained 81% of the variability of the IOFC calculated for the 135 original farms. When the number of farms below and above 1 standard deviation (SD) of the dRI were calculated, we found that 21

  15. HORIZONTAL BRANCH MORPHOLOGY OF GLOBULAR CLUSTERS: A MULTIVARIATE STATISTICAL ANALYSIS

    International Nuclear Information System (INIS)

    Jogesh Babu, G.; Chattopadhyay, Tanuka; Chattopadhyay, Asis Kumar; Mondal, Saptarshi

    2009-01-01

    The proper interpretation of horizontal branch (HB) morphology is crucial to the understanding of the formation history of stellar populations. In the present study a multivariate analysis is used (principal component analysis) for the selection of appropriate HB morphology parameter, which, in our case, is the logarithm of effective temperature extent of the HB (log T effHB ). Then this parameter is expressed in terms of the most significant observed independent parameters of Galactic globular clusters (GGCs) separately for coherent groups, obtained in a previous work, through a stepwise multiple regression technique. It is found that, metallicity ([Fe/H]), central surface brightness (μ v ), and core radius (r c ) are the significant parameters to explain most of the variations in HB morphology (multiple R 2 ∼ 0.86) for GGC elonging to the bulge/disk while metallicity ([Fe/H]) and absolute magnitude (M v ) are responsible for GGC belonging to the inner halo (multiple R 2 ∼ 0.52). The robustness is tested by taking 1000 bootstrap samples. A cluster analysis is performed for the red giant branch (RGB) stars of the GGC belonging to Galactic inner halo (Cluster 2). A multi-episodic star formation is preferred for RGB stars of GGC belonging to this group. It supports the asymptotic giant branch (AGB) model in three episodes instead of two as suggested by Carretta et al. for halo GGC while AGB model is suggested to be revisited for bulge/disk GGC.

  16. Multivariate Statistical Process Optimization in the Industrial Production of Enzymes

    DEFF Research Database (Denmark)

    Klimkiewicz, Anna

    of productyield. The potential of NIR technology to monitor the activity of the enzyme has beenthe subject of a feasibility study presented in PAPER I. It included (a) evaluation onwhich of the two real-time NIR flow cell configurations is the preferred arrangementfor monitoring of the retentate stream downstream...... strategies for theorganization of these datasets, with varying number of timestamps, into datastructures fit for latent variable (LV) modeling, have been compared. The ultimateaim of the data mining steps is the construction of statistical ‘soft models’ whichcapture the principle or latent behavior...

  17. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models

    NARCIS (Netherlands)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A.; van t Veld, Aart A.

    2012-01-01

    PURPOSE: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator

  18. A unifying framework for k-statistics, polykays and their multivariate generalizations.

    OpenAIRE

    DI NARDO, Elvira; GUARINO G, G.; Senato, D.

    2008-01-01

    Through the classical umbral calculus, we provide a unifying syntax for single and multivariate $k$-statistics, polykays and multivariate polykays. From a combinatorial point of view, we revisit the theory as exposed by Stuart and Ord, taking into account the Doubilet approach to symmetric functions. Moreover, by using exponential polynomials rather than set partitions, we provide a new formula for $k$-statistics that results in a very fast algorithm to generate such estimators.

  19. Advances in statistical monitoring of complex multivariate processes with applications in industrial process control

    CERN Document Server

    Kruger, Uwe

    2012-01-01

    The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike.  Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering.  The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applica

  20. Multivariate Statistical Methods as a Tool of Financial Analysis of Farm Business

    Czech Academy of Sciences Publication Activity Database

    Novák, J.; Sůvová, H.; Vondráček, Jiří

    2002-01-01

    Roč. 48, č. 1 (2002), s. 9-12 ISSN 0139-570X Institutional research plan: AV0Z1030915 Keywords : financial analysis * financial ratios * multivariate statistical methods * correlation analysis * discriminant analysis * cluster analysis Subject RIV: BB - Applied Statistics, Operational Research

  1. A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

    Science.gov (United States)

    2017-09-01

    application of statistical inference. Even when human forecasters leverage their professional experience, which is often gained through long periods of... application throughout statistics and Bayesian data analysis. The multivariate form of 2( , )  (e.g., Figure 12) is similarly analytically...data (i.e., no systematic manipulations with analytical functions), it is common in the statistical literature to apply mathematical transformations

  2. Relating N2O emissions during biological nitrogen removal with operating conditions using multivariate statistical techniques.

    Science.gov (United States)

    Vasilaki, V; Volcke, E I P; Nandi, A K; van Loosdrecht, M C M; Katsou, E

    2018-04-26

    Multivariate statistical analysis was applied to investigate the dependencies and underlying patterns between N 2 O emissions and online operational variables (dissolved oxygen and nitrogen component concentrations, temperature and influent flow-rate) during biological nitrogen removal from wastewater. The system under study was a full-scale reactor, for which hourly sensor data were available. The 15-month long monitoring campaign was divided into 10 sub-periods based on the profile of N 2 O emissions, using Binary Segmentation. The dependencies between operating variables and N 2 O emissions fluctuated according to Spearman's rank correlation. The correlation between N 2 O emissions and nitrite concentrations ranged between 0.51 and 0.78. Correlation >0.7 between N 2 O emissions and nitrate concentrations was observed at sub-periods with average temperature lower than 12 °C. Hierarchical k-means clustering and principal component analysis linked N 2 O emission peaks with precipitation events and ammonium concentrations higher than 2 mg/L, especially in sub-periods characterized by low N 2 O fluxes. Additionally, the highest ranges of measured N 2 O fluxes belonged to clusters corresponding with NO 3 -N concentration less than 1 mg/L in the upstream plug-flow reactor (middle of oxic zone), indicating slow nitrification rates. The results showed that the range of N 2 O emissions partially depends on the prior behavior of the system. The principal component analysis validated the findings from the clustering analysis and showed that ammonium, nitrate, nitrite and temperature explained a considerable percentage of the variance in the system for the majority of the sub-periods. The applied statistical methods, linked the different ranges of emissions with the system variables, provided insights on the effect of operating conditions on N 2 O emissions in each sub-period and can be integrated into N 2 O emissions data processing at wastewater treatment plants

  3. Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.

    Science.gov (United States)

    Taylor, Sandra L; Ruhaak, L Renee; Weiss, Robert H; Kelly, Karen; Kim, Kyoungmi

    2017-01-01

    High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results. We propose two multivariate two-part statistics that accommodate missing values and combine data from all biospecimens to identify differentially regulated compounds. Statistical significance is determined using a multivariate permutation null distribution. Relative to univariate tests, the multivariate procedures detected more significant compounds in three biological datasets. In a simulation study, we showed that multi-biospecimen testing procedures were more powerful than single-biospecimen methods when compounds are differentially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are differentially regulated in only one biospecimen. We provide R functions to implement and illustrate our method as supplementary information CONTACT: sltaylor@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. Multivariate statistical methods and data mining in particle physics (4/4)

    CERN Multimedia

    CERN. Geneva

    2008-01-01

    The lectures will cover multivariate statistical methods and their applications in High Energy Physics. The methods will be viewed in the framework of a statistical test, as used e.g. to discriminate between signal and background events. Topics will include an introduction to the relevant statistical formalism, linear test variables, neural networks, probability density estimation (PDE) methods, kernel-based PDE, decision trees and support vector machines. The methods will be evaluated with respect to criteria relevant to HEP analyses such as statistical power, ease of computation and sensitivity to systematic effects. Simple computer examples that can be extended to more complex analyses will be presented.

  5. Multivariate statistical methods and data mining in particle physics (2/4)

    CERN Multimedia

    CERN. Geneva

    2008-01-01

    The lectures will cover multivariate statistical methods and their applications in High Energy Physics. The methods will be viewed in the framework of a statistical test, as used e.g. to discriminate between signal and background events. Topics will include an introduction to the relevant statistical formalism, linear test variables, neural networks, probability density estimation (PDE) methods, kernel-based PDE, decision trees and support vector machines. The methods will be evaluated with respect to criteria relevant to HEP analyses such as statistical power, ease of computation and sensitivity to systematic effects. Simple computer examples that can be extended to more complex analyses will be presented.

  6. Multivariate statistical methods and data mining in particle physics (1/4)

    CERN Multimedia

    CERN. Geneva

    2008-01-01

    The lectures will cover multivariate statistical methods and their applications in High Energy Physics. The methods will be viewed in the framework of a statistical test, as used e.g. to discriminate between signal and background events. Topics will include an introduction to the relevant statistical formalism, linear test variables, neural networks, probability density estimation (PDE) methods, kernel-based PDE, decision trees and support vector machines. The methods will be evaluated with respect to criteria relevant to HEP analyses such as statistical power, ease of computation and sensitivity to systematic effects. Simple computer examples that can be extended to more complex analyses will be presented.

  7. Generalized Tensor-Based Morphometry of HIV/AIDS Using Multivariate Statistics on Deformation Tensors

    OpenAIRE

    Lepore, Natasha; Brun, Caroline; Chou, Yi-Yu; Chiang, Ming-Chang; Dutton, Rebecca A.; Hayashi, Kiralee M.; Luders, Eileen; Lopez, Oscar L.; Aizenstein, Howard J.; Toga, Arthur W.; Becker, James T.; Thompson, Paul M.

    2008-01-01

    This paper investigates the performance of a new multivariate method for tensor-based morphometry (TBM). Statistics on Riemannian manifolds are developed that exploit the full information in deformation tensor fields. In TBM, multiple brain images are warped to a common neuroanatomical template via 3-D nonlinear registration; the resulting deformation fields are analyzed statistically to identify group differences in anatomy. Rather than study the Jacobian determinant (volume expansion factor...

  8. Multivariate statistical treatment of PIXE analysis of some traditional Chinese medicines

    International Nuclear Information System (INIS)

    Xiaofeng Zhang; Jianguo Ma; Junfa Qin; Lun Xiao

    1991-01-01

    Elements in two kinds of 30 traditional Chinese medicines were analyzed by PIXE method, and the data were treated by multivariate statistical methods. The results show that these two kinds of traditional Chinese medicines are almost separable according to their elemental contents. The results are congruous with the traditional Chinese medicine practice. (author) 7 refs.; 2 figs.; 2 tabs

  9. Multivariate statistical evaluation of trace elements in groundwater in a coastal area in Shenzhen, China

    International Nuclear Information System (INIS)

    Chen Kouping; Jiao, Jiu J.; Huang Jianmin; Huang Runqiu

    2007-01-01

    Multivariate statistical techniques are efficient ways to display complex relationships among many objects. An attempt was made to study the data of trace elements in groundwater using multivariate statistical techniques such as principal component analysis (PCA), Q-mode factor analysis and cluster analysis. The original matrix consisted of 17 trace elements estimated from 55 groundwater samples colleted in 27 wells located in a coastal area in Shenzhen, China. PCA results show that trace elements of V, Cr, As, Mo, W, and U with greatest positive loadings typically occur as soluble oxyanions in oxidizing waters, while Mn and Co with greatest negative loadings are generally more soluble within oxygen depleted groundwater. Cluster analyses demonstrate that most groundwater samples collected from the same well in the study area during summer and winter still fall into the same group. This study also demonstrates the usefulness of multivariate statistical analysis in hydrochemical studies. - Multivariate statistical analysis was used to investigate relationships among trace elements and factors controlling trace element distribution in groundwater

  10. Monitoring a PVC batch process with multivariate statistical process control charts

    NARCIS (Netherlands)

    Tates, A. A.; Louwerse, D. J.; Smilde, A. K.; Koot, G. L. M.; Berndt, H.

    1999-01-01

    Multivariate statistical process control charts (MSPC charts) are developed for the industrial batch production process of poly(vinyl chloride) (PVC). With these MSPC charts different types of abnormal batch behavior were detected on-line. With batch contribution plots, the probable causes of these

  11. Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

    Science.gov (United States)

    Warner, Rebecca M.

    2007-01-01

    This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

  12. Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

    Science.gov (United States)

    Ma, Yan; Mazumdar, Madhu

    2011-10-30

    Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

  13. Multivariate statistical analysis for x-ray photoelectron spectroscopy spectral imaging: Effect of image acquisition time

    International Nuclear Information System (INIS)

    Peebles, D.E.; Ohlhausen, J.A.; Kotula, P.G.; Hutton, S.; Blomfield, C.

    2004-01-01

    The acquisition of spectral images for x-ray photoelectron spectroscopy (XPS) is a relatively new approach, although it has been used with other analytical spectroscopy tools for some time. This technique provides full spectral information at every pixel of an image, in order to provide a complete chemical mapping of the imaged surface area. Multivariate statistical analysis techniques applied to the spectral image data allow the determination of chemical component species, and their distribution and concentrations, with minimal data acquisition and processing times. Some of these statistical techniques have proven to be very robust and efficient methods for deriving physically realistic chemical components without input by the user other than the spectral matrix itself. The benefits of multivariate analysis of the spectral image data include significantly improved signal to noise, improved image contrast and intensity uniformity, and improved spatial resolution - which are achieved due to the effective statistical aggregation of the large number of often noisy data points in the image. This work demonstrates the improvements in chemical component determination and contrast, signal-to-noise level, and spatial resolution that can be obtained by the application of multivariate statistical analysis to XPS spectral images

  14. An Improved Rank Correlation Effect Size Statistic for Single-Case Designs: Baseline Corrected Tau.

    Science.gov (United States)

    Tarlow, Kevin R

    2017-07-01

    Measuring treatment effects when an individual's pretreatment performance is improving poses a challenge for single-case experimental designs. It may be difficult to determine whether improvement is due to the treatment or due to the preexisting baseline trend. Tau- U is a popular single-case effect size statistic that purports to control for baseline trend. However, despite its strengths, Tau- U has substantial limitations: Its values are inflated and not bound between -1 and +1, it cannot be visually graphed, and its relatively weak method of trend control leads to unacceptable levels of Type I error wherein ineffective treatments appear effective. An improved effect size statistic based on rank correlation and robust regression, Baseline Corrected Tau, is proposed and field-tested with both published and simulated single-case time series. A web-based calculator for Baseline Corrected Tau is also introduced for use by single-case investigators.

  15. Handling time misalignment and rank deficiency in liquid chromatography by multivariate curve resolution: Quantitation of five biogenic amines in fish

    International Nuclear Information System (INIS)

    Pinto, Licarion; Díaz Nieto, César Horacio; Zón, María Alicia; Fernández, Héctor; Ugulino de Araujo, Mario Cesar

    2016-01-01

    Biogenic amines (BAs) are used for identifying spoilage in food. The most common are tryptamine (TRY), 2-phenylethylamine (PHE), putrescine (PUT), cadaverine (CAD) and histamine (HIS). Due to lack of chromophores, chemical derivatization with dansyl was employed to analyze these BAs using high performance liquid chromatography with a diode array detector (HPLC-DAD). However, the derivatization reaction occurs with any primary or secondary amine, leading to co-elution of analytes and interferents with identical spectral profiles, and thus causing rank deficiency. When the spectral profile is the same and peak misalignment is present on the chromatographic runs, it is not possible to handle the data only with Multivariate Curve Resolution and Alternative Least Square (MCR-ALS), by augmenting the time, or the spectral mode. A way to circumvent this drawback is to receive information from another detector that leads to a selective profile for the analyte. To overcome both problems, (tri-linearity break in time, and spectral mode), this paper proposes a new analytical methodology for fast quantitation of these BAs in fish with HPLC-DAD by using the icoshift algorithm for temporal misalignment correction before MCR-ALS spectral mode augmented treatment. Limits of detection, relative errors of prediction (REP) and average recoveries, ranging from 0.14 to 0.50 µg mL"−"1, 3.5–8.8% and 88.08%–99.68%, respectively. These are outstanding results obtained, reaching quantification limits for the five BAs much lower than those established by the Food and Agriculture Organization of the United Nations and World Health Organization (FAO/WHO), and the European Food Safety Authority (EFSA), all without any pre-concentration steps. The concentrations of BAs in fish samples ranged from 7.82 to 29.41 µg g"−"1, 8.68–25.95 µg g"−"1, 4.76–28.54 µg g"−"1, 5.18–39.95 µg g"−"1 and 1.45–52.62 µg g"−"1 for TRY, PHE, PUT, CAD, and HIS, respectively. In

  16. Handling time misalignment and rank deficiency in liquid chromatography by multivariate curve resolution: Quantitation of five biogenic amines in fish

    Energy Technology Data Exchange (ETDEWEB)

    Pinto, Licarion [Laboratório de Automação e Instrumentação em Química Analítica e Quimiometria (LAQA), Universidade Federal da Paraíba, CCEN, Departamento de Química, Caixa Postal 5093, CEP 58051-970, João Pessoa, PB (Brazil); Díaz Nieto, César Horacio; Zón, María Alicia; Fernández, Héctor [Departamento de Química, Facultad de Ciencias Exactas, Físico-Químicas y Naturales, Universidad Nacional de Río Cuarto, 5800, Río Cuarto (Argentina); Ugulino de Araujo, Mario Cesar, E-mail: laqa@quimica.ufpb.br [Laboratório de Automação e Instrumentação em Química Analítica e Quimiometria (LAQA), Universidade Federal da Paraíba, CCEN, Departamento de Química, Caixa Postal 5093, CEP 58051-970, João Pessoa, PB (Brazil)

    2016-01-01

    Biogenic amines (BAs) are used for identifying spoilage in food. The most common are tryptamine (TRY), 2-phenylethylamine (PHE), putrescine (PUT), cadaverine (CAD) and histamine (HIS). Due to lack of chromophores, chemical derivatization with dansyl was employed to analyze these BAs using high performance liquid chromatography with a diode array detector (HPLC-DAD). However, the derivatization reaction occurs with any primary or secondary amine, leading to co-elution of analytes and interferents with identical spectral profiles, and thus causing rank deficiency. When the spectral profile is the same and peak misalignment is present on the chromatographic runs, it is not possible to handle the data only with Multivariate Curve Resolution and Alternative Least Square (MCR-ALS), by augmenting the time, or the spectral mode. A way to circumvent this drawback is to receive information from another detector that leads to a selective profile for the analyte. To overcome both problems, (tri-linearity break in time, and spectral mode), this paper proposes a new analytical methodology for fast quantitation of these BAs in fish with HPLC-DAD by using the icoshift algorithm for temporal misalignment correction before MCR-ALS spectral mode augmented treatment. Limits of detection, relative errors of prediction (REP) and average recoveries, ranging from 0.14 to 0.50 µg mL{sup −1}, 3.5–8.8% and 88.08%–99.68%, respectively. These are outstanding results obtained, reaching quantification limits for the five BAs much lower than those established by the Food and Agriculture Organization of the United Nations and World Health Organization (FAO/WHO), and the European Food Safety Authority (EFSA), all without any pre-concentration steps. The concentrations of BAs in fish samples ranged from 7.82 to 29.41 µg g{sup −1}, 8.68–25.95 µg g{sup −1}, 4.76–28.54 µg g{sup −1}, 5.18–39.95 µg g{sup −1} and 1.45–52.62 µg g{sup −1} for TRY, PHE, PUT, CAD, and

  17. Handling time misalignment and rank deficiency in liquid chromatography by multivariate curve resolution: Quantitation of five biogenic amines in fish.

    Science.gov (United States)

    Pinto, Licarion; Díaz Nieto, César Horacio; Zón, María Alicia; Fernández, Héctor; de Araujo, Mario Cesar Ugulino

    2016-01-01

    Biogenic amines (BAs) are used for identifying spoilage in food. The most common are tryptamine (TRY), 2-phenylethylamine (PHE), putrescine (PUT), cadaverine (CAD) and histamine (HIS). Due to lack of chromophores, chemical derivatization with dansyl was employed to analyze these BAs using high performance liquid chromatography with a diode array detector (HPLC-DAD). However, the derivatization reaction occurs with any primary or secondary amine, leading to co-elution of analytes and interferents with identical spectral profiles, and thus causing rank deficiency. When the spectral profile is the same and peak misalignment is present on the chromatographic runs, it is not possible to handle the data only with Multivariate Curve Resolution and Alternative Least Square (MCR-ALS), by augmenting the time, or the spectral mode. A way to circumvent this drawback is to receive information from another detector that leads to a selective profile for the analyte. To overcome both problems, (tri-linearity break in time, and spectral mode), this paper proposes a new analytical methodology for fast quantitation of these BAs in fish with HPLC-DAD by using the icoshift algorithm for temporal misalignment correction before MCR-ALS spectral mode augmented treatment. Limits of detection, relative errors of prediction (REP) and average recoveries, ranging from 0.14 to 0.50 µg mL(-1), 3.5-8.8% and 88.08%-99.68%, respectively. These are outstanding results obtained, reaching quantification limits for the five BAs much lower than those established by the Food and Agriculture Organization of the United Nations and World Health Organization (FAO/WHO), and the European Food Safety Authority (EFSA), all without any pre-concentration steps. The concentrations of BAs in fish samples ranged from 7.82 to 29.41 µg g(-1), 8.68-25.95 µg g(-1), 4.76-28.54 µg g(-1), 5.18-39.95 µg g(-1) and 1.45-52.62 µg g(-1) for TRY, PHE, PUT, CAD, and HIS, respectively. In addition, the proposed method spends

  18. Application of Multivariable Statistical Techniques in Plant-wide WWTP Control Strategies Analysis

    DEFF Research Database (Denmark)

    Flores Alsina, Xavier; Comas, J.; Rodríguez-Roda, I.

    2007-01-01

    The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant...... analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii......) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation...

  19. Provenance Study of Archaeological Ceramics from Syria Using XRF Multivariate Statistical Analysis and Thermoluminescence Dating

    OpenAIRE

    Bakraji, Elias Hanna; Abboud, Rana; Issa, Haissm

    2014-01-01

    Thermoluminescence (TL) dating and multivariate statistical methods based on radioisotope X-ray fluorescence analysis have been utilized to date and classify Syrian archaeological ceramics fragment from Tel Jamous site. 54 samples were analyzed by radioisotope X-ray fluorescence; 51 of them come from Tel Jamous archaeological site in Sahel Akkar region, Syria, which fairly represent ceramics belonging to the Middle Bronze Age (2150 to 1600 B.C.) and the remaining three samples come from Mar-T...

  20. A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series

    Directory of Open Access Journals (Sweden)

    Charmaine eDemanuele

    2015-10-01

    Full Text Available Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from fMRI blood oxygenation level dependent (BOLD time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC, but not in the primary visual cortex (V1. Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel

  1. Pattern recognition by the use of multivariate statistical evaluation of macro- and micro-PIXE results

    International Nuclear Information System (INIS)

    Tapper, U.A.S.; Malmqvist, K.G.; Loevestam, N.E.G.; Swietlicki, E.; Salford, L.G.

    1991-01-01

    The importance of statistical evaluation of multielemental data is illustrated using the data collected in a macro- and micro-PIXE analysis of human brain tumours. By employing a multivariate statistical classification methodology (SIMCA) it was shown that the total information collected from each specimen separates three types of tissue: High malignant, less malignant and normal brain tissue. This makes a classification of a given specimen possible based on the elemental concentrations. Partial least squares regression (PLS), a multivariate regression method, made it possible to study the relative importance of the examined nine trace elements, the dry/wet weight ratio and the age of the patient in predicting the survival time after operation for patients with the high malignant form, astrocytomas grade III-IV. The elemental maps from a microprobe analysis were also subjected to multivariate analysis. This showed that the six elements sorted into maps could be presented in three maps containing all the relevant information. The intensity in these maps is proportional to the value (score) of the actual pixel along the calculated principal components. (orig.)

  2. Generalized tensor-based morphometry of HIV/AIDS using multivariate statistics on deformation tensors.

    Science.gov (United States)

    Lepore, N; Brun, C; Chou, Y Y; Chiang, M C; Dutton, R A; Hayashi, K M; Luders, E; Lopez, O L; Aizenstein, H J; Toga, A W; Becker, J T; Thompson, P M

    2008-01-01

    This paper investigates the performance of a new multivariate method for tensor-based morphometry (TBM). Statistics on Riemannian manifolds are developed that exploit the full information in deformation tensor fields. In TBM, multiple brain images are warped to a common neuroanatomical template via 3-D nonlinear registration; the resulting deformation fields are analyzed statistically to identify group differences in anatomy. Rather than study the Jacobian determinant (volume expansion factor) of these deformations, as is common, we retain the full deformation tensors and apply a manifold version of Hotelling's $T(2) test to them, in a Log-Euclidean domain. In 2-D and 3-D magnetic resonance imaging (MRI) data from 26 HIV/AIDS patients and 14 matched healthy subjects, we compared multivariate tensor analysis versus univariate tests of simpler tensor-derived indices: the Jacobian determinant, the trace, geodesic anisotropy, and eigenvalues of the deformation tensor, and the angle of rotation of its eigenvectors. We detected consistent, but more extensive patterns of structural abnormalities, with multivariate tests on the full tensor manifold. Their improved power was established by analyzing cumulative p-value plots using false discovery rate (FDR) methods, appropriately controlling for false positives. This increased detection sensitivity may empower drug trials and large-scale studies of disease that use tensor-based morphometry.

  3. Assessment of Surface Water Quality Using Multivariate Statistical Techniques in the Terengganu River Basin

    International Nuclear Information System (INIS)

    Aminu Ibrahim; Hafizan Juahir; Mohd Ekhwan Toriman; Mustapha, A.; Azman Azid; Isiyaka, H.A.

    2015-01-01

    Multivariate Statistical techniques including cluster analysis, discriminant analysis, and principal component analysis/factor analysis were applied to investigate the spatial variation and pollution sources in the Terengganu river basin during 5 years of monitoring 13 water quality parameters at thirteen different stations. Cluster analysis (CA) classified 13 stations into 2 clusters low polluted (LP) and moderate polluted (MP) based on similar water quality characteristics. Discriminant analysis (DA) rendered significant data reduction with 4 parameters (pH, NH 3 -NL, PO 4 and EC) and correct assignation of 95.80 %. The PCA/ FA applied to the data sets, yielded in five latent factors accounting 72.42 % of the total variance in the water quality data. The obtained varifactors indicate that parameters in charge for water quality variations are mainly related to domestic waste, industrial, runoff and agricultural (anthropogenic activities). Therefore, multivariate techniques are important in environmental management. (author)

  4. Study on loss detection algorithms for tank monitoring data using multivariate statistical analysis

    International Nuclear Information System (INIS)

    Suzuki, Mitsutoshi; Burr, Tom

    2009-01-01

    Evaluation of solution monitoring data to support material balance evaluation was proposed about a decade ago because of concerns regarding the large throughput planned at Rokkasho Reprocessing Plant (RRP). A numerical study using the simulation code (FACSIM) was done and significant increases in the detection probabilities (DP) for certain types of losses were shown. To be accepted internationally, it is very important to verify such claims using real solution monitoring data. However, a demonstrative study with real tank data has not been carried out due to the confidentiality of the tank data. This paper describes an experimental study that has been started using actual data from the Solution Measurement and Monitoring System (SMMS) in the Tokai Reprocessing Plant (TRP) and the Savannah River Site (SRS). Multivariate statistical methods, such as a vector cumulative sum and a multi-scale statistical analysis, have been applied to the real tank data that have superimposed simulated loss. Although quantitative conclusions have not been derived for the moment due to the difficulty of baseline evaluation, the multivariate statistical methods remain promising for abrupt and some types of protracted loss detection. (author)

  5. An Improvement of the Hotelling T2 Statistic in Monitoring Multivariate Quality Characteristics

    Directory of Open Access Journals (Sweden)

    Ashkan Shabbak

    2012-01-01

    Full Text Available The Hotelling T2 statistic is the most popular statistic used in multivariate control charts to monitor multiple qualities. However, this statistic is easily affected by the existence of more than one outlier in the data set. To rectify this problem, robust control charts, which are based on the minimum volume ellipsoid and the minimum covariance determinant, have been proposed. Most researchers assess the performance of multivariate control charts based on the number of signals without paying much attention to whether those signals are really outliers. With due respect, we propose to evaluate control charts not only based on the number of detected outliers but also with respect to their correct positions. In this paper, an Upper Control Limit based on the median and the median absolute deviation is also proposed. The results of this study signify that the proposed Upper Control Limit improves the detection of correct outliers but that it suffers from a swamping effect when the positions of outliers are not taken into consideration. Finally, a robust control chart based on the diagnostic robust generalised potential procedure is introduced to remedy this drawback.

  6. Application of instrumental neutron activation analysis and multivariate statistical methods to archaeological Syrian ceramics

    International Nuclear Information System (INIS)

    Bakraji, E. H.; Othman, I.; Sarhil, A.; Al-Somel, N.

    2002-01-01

    Instrumental neutron activation analysis (INAA) has been utilized in the analysis of thirty-seven archaeological ceramics fragment samples collected from Tal AI-Wardiate site, Missiaf town, Hamma city, Syria. 36 chemical elements were determined. These elemental concentrations have been processed using two multivariate statistical methods, cluster and factor analysis in order to determine similarities and correlation between the various samples. Factor analysis confirms that samples were correctly classified by cluster analysis. The results showed that samples can be considered to be manufactured using three different sources of raw material. (author)

  7. Testing University Rankings Statistically: Why this Perhaps is not such a Good Idea after All. Some Reflections on Statistical Power, Effect Size, Random Sampling and Imaginary Populations

    DEFF Research Database (Denmark)

    Schneider, Jesper Wiborg

    2012-01-01

    In this paper we discuss and question the use of statistical significance tests in relation to university rankings as recently suggested. We outline the assumptions behind and interpretations of statistical significance tests and relate this to examples from the recent SCImago Institutions Rankin...

  8. The intervals method: a new approach to analyse finite element outputs using multivariate statistics

    Directory of Open Access Journals (Sweden)

    Jordi Marcé-Nogué

    2017-10-01

    Full Text Available Background In this paper, we propose a new method, named the intervals’ method, to analyse data from finite element models in a comparative multivariate framework. As a case study, several armadillo mandibles are analysed, showing that the proposed method is useful to distinguish and characterise biomechanical differences related to diet/ecomorphology. Methods The intervals’ method consists of generating a set of variables, each one defined by an interval of stress values. Each variable is expressed as a percentage of the area of the mandible occupied by those stress values. Afterwards these newly generated variables can be analysed using multivariate methods. Results Applying this novel method to the biological case study of whether armadillo mandibles differ according to dietary groups, we show that the intervals’ method is a powerful tool to characterize biomechanical performance and how this relates to different diets. This allows us to positively discriminate between specialist and generalist species. Discussion We show that the proposed approach is a useful methodology not affected by the characteristics of the finite element mesh. Additionally, the positive discriminating results obtained when analysing a difficult case study suggest that the proposed method could be a very useful tool for comparative studies in finite element analysis using multivariate statistical approaches.

  9. The intervals method: a new approach to analyse finite element outputs using multivariate statistics

    Science.gov (United States)

    De Esteban-Trivigno, Soledad; Püschel, Thomas A.; Fortuny, Josep

    2017-01-01

    Background In this paper, we propose a new method, named the intervals’ method, to analyse data from finite element models in a comparative multivariate framework. As a case study, several armadillo mandibles are analysed, showing that the proposed method is useful to distinguish and characterise biomechanical differences related to diet/ecomorphology. Methods The intervals’ method consists of generating a set of variables, each one defined by an interval of stress values. Each variable is expressed as a percentage of the area of the mandible occupied by those stress values. Afterwards these newly generated variables can be analysed using multivariate methods. Results Applying this novel method to the biological case study of whether armadillo mandibles differ according to dietary groups, we show that the intervals’ method is a powerful tool to characterize biomechanical performance and how this relates to different diets. This allows us to positively discriminate between specialist and generalist species. Discussion We show that the proposed approach is a useful methodology not affected by the characteristics of the finite element mesh. Additionally, the positive discriminating results obtained when analysing a difficult case study suggest that the proposed method could be a very useful tool for comparative studies in finite element analysis using multivariate statistical approaches. PMID:29043107

  10. A multivariate statistical study on a diversified data gathering system for nuclear power plants

    International Nuclear Information System (INIS)

    Samanta, P.K.; Teichmann, T.; Levine, M.M.; Kato, W.Y.

    1989-02-01

    In this report, multivariate statistical methods are presented and applied to demonstrate their use in analyzing nuclear power plant operational data. For analyses of nuclear power plant events, approaches are presented for detecting malfunctions and degradations within the course of the event. At the system level, approaches are investigated as a means of diagnosis of system level performance. This involves the detection of deviations from normal performance of the system. The input data analyzed are the measurable physical parameters, such as steam generator level, pressurizer water level, auxiliary feedwater flow, etc. The study provides the methodology and illustrative examples based on data gathered from simulation of nuclear power plant transients and computer simulation of a plant system performance (due to lack of easily accessible operational data). Such an approach, once fully developed, can be used to explore statistically the detection of failure trends and patterns and prevention of conditions with serious safety implications. 33 refs., 18 figs., 9 tabs

  11. Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

    International Nuclear Information System (INIS)

    Belianinov, Alex; Ganesh, Panchapakesan; Lin, Wenzhi; Jesse, Stephen; Pan, Minghu; Kalinin, Sergei V.; Sales, Brian C.; Sefat, Athena S.

    2014-01-01

    Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe 0.55 Se 0.45 (T c = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe 1−x Se x structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces

  12. Multivariate statistical analysis to investigate the subduction zone parameters favoring the occurrence of giant megathrust earthquakes

    Science.gov (United States)

    Brizzi, S.; Sandri, L.; Funiciello, F.; Corbi, F.; Piromallo, C.; Heuret, A.

    2018-03-01

    The observed maximum magnitude of subduction megathrust earthquakes is highly variable worldwide. One key question is which conditions, if any, favor the occurrence of giant earthquakes (Mw ≥ 8.5). Here we carry out a multivariate statistical study in order to investigate the factors affecting the maximum magnitude of subduction megathrust earthquakes. We find that the trench-parallel extent of subduction zones and the thickness of trench sediments provide the largest discriminating capability between subduction zones that have experienced giant earthquakes and those having significantly lower maximum magnitude. Monte Carlo simulations show that the observed spatial distribution of giant earthquakes cannot be explained by pure chance to a statistically significant level. We suggest that the combination of a long subduction zone with thick trench sediments likely promotes a great lateral rupture propagation, characteristic of almost all giant earthquakes.

  13. Application of multivariate statistical analysis in the pollution and health risk of traffic-related heavy metals.

    Science.gov (United States)

    Ebqa'ai, Mohammad; Ibrahim, Bashar

    2017-12-01

    This study aims to analyse the heavy metal pollutants in Jeddah, the second largest city in the Gulf Cooperation Council with a population exceeding 3.5 million, and many vehicles. Ninety-eight street dust samples were collected seasonally from the six major roads as well as the Jeddah Beach, and subsequently digested using modified Leeds Public Analyst method. The heavy metals (Fe, Zn, Mn, Cu, Cd, and Pb) were extracted from the ash using methyl isobutyl ketone as solvent extraction and eventually analysed by atomic absorption spectroscopy. Multivariate statistical techniques, principal component analysis (PCA), and hierarchical cluster analysis were applied to these data. Heavy metal concentrations were ranked according to the following descending order: Fe > Zn > Mn > Cu > Pb > Cd. In order to study the pollution and health risk from these heavy metals as well as estimating their effect on the environment, pollution indices, integrated pollution index, enrichment factor, daily dose average, hazard quotient, and hazard index were all analysed. The PCA showed high levels of Zn, Fe, and Cd in Al Kurnish road, while these elements were consistently detected on King Abdulaziz and Al Madina roads. The study indicates that high levels of Zn and Pb pollution were recorded for major roads in Jeddah. Six out of seven roads had high pollution indices. This study is the first step towards further investigations into current health problems in Jeddah, such as anaemia and asthma.

  14. Quantitative Evaluation of Hybrid Aspen Xylem and Immunolabeling Patterns Using Image Analysis and Multivariate Statistics

    Directory of Open Access Journals (Sweden)

    David Sandquist

    2015-06-01

    Full Text Available A new method is presented for quantitative evaluation of hybrid aspen genotype xylem morphology and immunolabeling micro-distribution. This method can be used as an aid in assessing differences in genotypes from classic tree breeding studies, as well as genetically engineered plants. The method is based on image analysis, multivariate statistical evaluation of light, and immunofluorescence microscopy images of wood xylem cross sections. The selected immunolabeling antibodies targeted five different epitopes present in aspen xylem cell walls. Twelve down-regulated hybrid aspen genotypes were included in the method development. The 12 knock-down genotypes were selected based on pre-screening by pyrolysis-IR of global chemical content. The multivariate statistical evaluations successfully identified comparative trends for modifications in the down-regulated genotypes compared to the unmodified control, even when no definitive conclusions could be drawn from individual studied variables alone. Of the 12 genotypes analyzed, three genotypes showed significant trends for modifications in both morphology and immunolabeling. Six genotypes showed significant trends for modifications in either morphology or immunocoverage. The remaining three genotypes did not show any significant trends for modification.

  15. Point defect characterization in HAADF-STEM images using multivariate statistical analysis

    International Nuclear Information System (INIS)

    Sarahan, Michael C.; Chi, Miaofang; Masiel, Daniel J.; Browning, Nigel D.

    2011-01-01

    Quantitative analysis of point defects is demonstrated through the use of multivariate statistical analysis. This analysis consists of principal component analysis for dimensional estimation and reduction, followed by independent component analysis to obtain physically meaningful, statistically independent factor images. Results from these analyses are presented in the form of factor images and scores. Factor images show characteristic intensity variations corresponding to physical structure changes, while scores relate how much those variations are present in the original data. The application of this technique is demonstrated on a set of experimental images of dislocation cores along a low-angle tilt grain boundary in strontium titanate. A relationship between chemical composition and lattice strain is highlighted in the analysis results, with picometer-scale shifts in several columns measurable from compositional changes in a separate column. -- Research Highlights: → Multivariate analysis of HAADF-STEM images. → Distinct structural variations among SrTiO 3 dislocation cores. → Picometer atomic column shifts correlated with atomic column population changes.

  16. Multivariate statistical analyses demonstrate unique host immune responses to single and dual lentiviral infection.

    Directory of Open Access Journals (Sweden)

    Sunando Roy

    2009-10-01

    Full Text Available Feline immunodeficiency virus (FIV and human immunodeficiency virus (HIV are recently identified lentiviruses that cause progressive immune decline and ultimately death in infected cats and humans. It is of great interest to understand how to prevent immune system collapse caused by these lentiviruses. We recently described that disease caused by a virulent FIV strain in cats can be attenuated if animals are first infected with a feline immunodeficiency virus derived from a wild cougar. The detailed temporal tracking of cat immunological parameters in response to two viral infections resulted in high-dimensional datasets containing variables that exhibit strong co-variation. Initial analyses of these complex data using univariate statistical techniques did not account for interactions among immunological response variables and therefore potentially obscured significant effects between infection state and immunological parameters.Here, we apply a suite of multivariate statistical tools, including Principal Component Analysis, MANOVA and Linear Discriminant Analysis, to temporal immunological data resulting from FIV superinfection in domestic cats. We investigated the co-variation among immunological responses, the differences in immune parameters among four groups of five cats each (uninfected, single and dual infected animals, and the "immune profiles" that discriminate among them over the first four weeks following superinfection. Dual infected cats mount an immune response by 24 days post superinfection that is characterized by elevated levels of CD8 and CD25 cells and increased expression of IL4 and IFNgamma, and FAS. This profile discriminates dual infected cats from cats infected with FIV alone, which show high IL-10 and lower numbers of CD8 and CD25 cells.Multivariate statistical analyses demonstrate both the dynamic nature of the immune response to FIV single and dual infection and the development of a unique immunological profile in dual

  17. Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

    Science.gov (United States)

    Sayemuzzaman, M.; Ye, M.

    2015-12-01

    The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface

  18. Multivariate statistical analysis of radioactive variables in two phosphate ores from Sudan

    International Nuclear Information System (INIS)

    Adam, Abdel Majid A.; Eltayeb, Mohamed Ahmed H.

    2012-01-01

    Multivariate statistical techniques are efficient ways to display complex relationships among many objects. An attempt was made to study the radioactive data in two types of Sudanese phosphate deposits; Kurun and Uro phosphate, using several multivariate statistical methods. Pearson correlation coefficient revealed that a U-238 distribution in Kurun phosphate is controlled by the variation of K-40 concentration, whereas in Uro phosphate it is controlled by the variation of U-235 and U-234 concentration. Histograms and normal Q–Q plots clearly show that the radioactive variables did not follow a normal distribution. This non-normality feature observed may be attributed to complicating influence of geological factors. The principal components analysis (PCA) gives a model of five components for representing the acquired data from Kurun phosphate, where 89.5% of the total variance is explained. A model of four components was sufficient to represent the acquired data from Uro phosphate, where 87.5% of the total data variance is explained. The hierarchical cluster analysis (HCA) indicates that U-238 behaves in the same manner in the two types of phosphates; it associated with a group of four radionuclides; U-234, Po-210, Ra-226, Th-230, which the most abundant radionuclides, and all belong to the uranium-238 decay series. Two parameters have been adapted for the direct differentiate between the two phosphates. Firstly, U-238 in Uro phosphate have shown higher degree of mobility (CV% = 82.6) than that in Kurun phosphate (CV% = 64.7), and secondly, the activity ratio of Th-230/Th-232 in Uro phosphate is nine times than that in Kurun phosphate. - Highlights: ► Multivariate statistical techniques were used to characterize radioactive data. ► U-238 in Uro phosphate shows higher degree of mobility (CV% = 82.6). ► U-238 in Kurun phosphate shows lower degree of mobility (CV% = 64.7). ► The radioactive variables did not follow a normal distribution. ► The ratio of Th

  19. A multivariate statistical study with a factor analysis of recent planktonic foraminiferal distribution in the Coromandel Coast of India

    Digital Repository Service at National Institute of Oceanography (India)

    Jayalakshmy, K.V.; Rao, K.K.

    A study of planktonic foraminiferal assemblages from 19 stations in the neritic and oceanic regions off the Coromandel Coast, Bay of Bengal has been made using a multivariate statistical method termed as factor analysis. On the basis of abundance...

  20. Forensic analysis of Salvia divinorum using multivariate statistical procedures. Part I: discrimination from related Salvia species.

    Science.gov (United States)

    Willard, Melissa A Bodnar; McGuffin, Victoria L; Smith, Ruth Waddell

    2012-01-01

    Salvia divinorum is a hallucinogenic herb that is internationally regulated. In this study, salvinorin A, the active compound in S. divinorum, was extracted from S. divinorum plant leaves using a 5-min extraction with dichloromethane. Four additional Salvia species (Salvia officinalis, Salvia guaranitica, Salvia splendens, and Salvia nemorosa) were extracted using this procedure, and all extracts were analyzed by gas chromatography-mass spectrometry. Differentiation of S. divinorum from other Salvia species was successful based on visual assessment of the resulting chromatograms. To provide a more objective comparison, the total ion chromatograms (TICs) were subjected to principal components analysis (PCA). Prior to PCA, the TICs were subjected to a series of data pretreatment procedures to minimize non-chemical sources of variance in the data set. Successful discrimination of S. divinorum from the other four Salvia species was possible based on visual assessment of the PCA scores plot. To provide a numerical assessment of the discrimination, a series of statistical procedures such as Euclidean distance measurement, hierarchical cluster analysis, Student's t tests, Wilcoxon rank-sum tests, and Pearson product moment correlation were also applied to the PCA scores. The statistical procedures were then compared to determine the advantages and disadvantages for forensic applications.

  1. Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry

    OpenAIRE

    Bersimis, Sotiris; Panaretos, John; Psarakis, Stelios

    2005-01-01

    Woodall and Montgomery [35] in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality - process characteristics is necessary. Process monitoring problems in which several related variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC).This ...

  2. Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

    Energy Technology Data Exchange (ETDEWEB)

    Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van' t [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands)

    2012-03-15

    Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

  3. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

    Science.gov (United States)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A

    2012-03-15

    To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright © 2012 Elsevier Inc. All rights reserved.

  4. Source Evaluation and Trace Metal Contamination in Benthic Sediments from Equatorial Ecosystems Using Multivariate Statistical Techniques.

    Directory of Open Access Journals (Sweden)

    Nsikak U Benson

    Full Text Available Trace metals (Cd, Cr, Cu, Ni and Pb concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria. The degree of contamination was assessed using the individual contamination factors (ICF and global contamination factor (GCF. Multivariate statistical approaches including principal component analysis (PCA, cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources.

  5. Water quality, Multivariate statistical techniques, submarine out fall, spatial variation, temporal variation

    International Nuclear Information System (INIS)

    Garcia, Francisco; Palacio, Carlos; Garcia, Uriel

    2012-01-01

    Multivariate statistical techniques were used to investigate the temporal and spatial variations of water quality at the Santa Marta coastal area where a submarine out fall that discharges 1 m3/s of domestic wastewater is located. Two-way analysis of variance (ANOVA), cluster and principal component analysis and Krigging interpolation were considered for this report. Temporal variation showed two heterogeneous periods. From December to April, and July, where the concentration of the water quality parameters is higher; the rest of the year (May, June, August-November) were significantly lower. The spatial variation reported two areas where the water quality is different, this difference is related to the proximity to the submarine out fall discharge.

  6. Multivariate statistical analysis of stream sediments for mineral resources from the Craig NTMS Quadrangle, Colorado

    International Nuclear Information System (INIS)

    Beyth, M.; McInteer, C.; Broxton, D.E.; Bolivar, S.L.; Luke, M.E.

    1980-06-01

    Multivariate statistical analyses were carried out on Hydrogeochemical and Stream Sediment Reconnaissance data from the Craig quadrangle, Colorado, to support the National Uranium Resource Evaluation and to evaluate strategic or other important commercial mineral resources. A few areas for favorable uranium mineralization are suggested for parts of the Wyoming Basin, Park Range, and Gore Range. Six potential source rocks for uranium are postulated based on factor score mapping. Vanadium in stream sediments is suggested as a pathfinder for carnotite-type mineralization. A probable northwest trend of lead-zinc-copper mineralization associated with Tertiary intrusions is suggested. A few locations are mapped where copper is associated with cobalt. Concentrations of placer sands containing rare earth elements, probably of commercial value, are indicated for parts of the Sand Wash Basin

  7. Forensic classification of counterfeit banknote paper by X-ray fluorescence and multivariate statistical methods.

    Science.gov (United States)

    Guo, Hongling; Yin, Baohua; Zhang, Jie; Quan, Yangke; Shi, Gaojun

    2016-09-01

    Counterfeiting of banknotes is a crime and seriously harmful to economy. Examination of the paper, ink and toners used to make counterfeit banknotes can provide useful information to classify and link different cases in which the suspects use the same raw materials. In this paper, 21 paper samples of counterfeit banknotes seized from 13 cases were analyzed by wavelength dispersive X-ray fluorescence. After measuring the elemental composition in paper semi-quantitatively, the normalized weight percentage data of 10 elements were processed by multivariate statistical methods of cluster analysis and principle component analysis. All these paper samples were mainly classified into 3 groups. Nine separate cases were successfully linked. It is demonstrated that elemental composition measured by XRF is a useful way to compare and classify papers used in different cases. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  8. Application of multivariate statistical techniques in the water quality assessment of Danube river, Serbia

    Directory of Open Access Journals (Sweden)

    Voza Danijela

    2015-12-01

    Full Text Available The aim of this article is to evaluate the quality of the Danube River in its course through Serbia as well as to demonstrate the possibilities for using three statistical methods: Principal Component Analysis (PCA, Factor Analysis (FA and Cluster Analysis (CA in the surface water quality management. Given that the Danube is an important trans-boundary river, thorough water quality monitoring by sampling at different distances during shorter and longer periods of time is not only ecological, but also a political issue. Monitoring was carried out at monthly intervals from January to December 2011, at 17 sampling sites. The obtained data set was treated by multivariate techniques in order, firstly, to identify the similarities and differences between sampling periods and locations, secondly, to recognize variables that affect the temporal and spatial water quality changes and thirdly, to present the anthropogenic impact on water quality parameters.

  9. Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

    International Nuclear Information System (INIS)

    Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van’t

    2012-01-01

    Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

  10. Assessment of metals bioavailability to vegetables under field conditions using DGT, single extractions and multivariate statistics

    Science.gov (United States)

    2012-01-01

    Background The metals bioavailability in soils is commonly assessed by chemical extractions; however a generally accepted method is not yet established. In this study, the effectiveness of Diffusive Gradients in Thin-films (DGT) technique and single extractions in the assessment of metals bioaccumulation in vegetables, and the influence of soil parameters on phytoavailability were evaluated using multivariate statistics. Soil and plants grown in vegetable gardens from mining-affected rural areas, NW Romania, were collected and analysed. Results Pseudo-total metal content of Cu, Zn and Cd in soil ranged between 17.3-146 mg kg-1, 141–833 mg kg-1 and 0.15-2.05 mg kg-1, respectively, showing enriched contents of these elements. High degrees of metals extractability in 1M HCl and even in 1M NH4Cl were observed. Despite the relatively high total metal concentrations in soil, those found in vegetables were comparable to values typically reported for agricultural crops, probably due to the low concentrations of metals in soil solution (Csoln) and low effective concentrations (CE), assessed by DGT technique. Among the analysed vegetables, the highest metal concentrations were found in carrots roots. By applying multivariate statistics, it was found that CE, Csoln and extraction in 1M NH4Cl, were better predictors for metals bioavailability than the acid extractions applied in this study. Copper transfer to vegetables was strongly influenced by soil organic carbon (OC) and cation exchange capacity (CEC), while pH had a higher influence on Cd transfer from soil to plants. Conclusions The results showed that DGT can be used for general evaluation of the risks associated to soil contamination with Cu, Zn and Cd in field conditions. Although quantitative information on metals transfer from soil to vegetables was not observed. PMID:23079133

  11. A fast algorithm for determining bounds and accurate approximate p-values of the rank product statistic for replicate experiments.

    Science.gov (United States)

    Heskes, Tom; Eisinga, Rob; Breitling, Rainer

    2014-11-21

    The rank product method is a powerful statistical technique for identifying differentially expressed molecules in replicated experiments. A critical issue in molecule selection is accurate calculation of the p-value of the rank product statistic to adequately address multiple testing. Both exact calculation and permutation and gamma approximations have been proposed to determine molecule-level significance. These current approaches have serious drawbacks as they are either computationally burdensome or provide inaccurate estimates in the tail of the p-value distribution. We derive strict lower and upper bounds to the exact p-value along with an accurate approximation that can be used to assess the significance of the rank product statistic in a computationally fast manner. The bounds and the proposed approximation are shown to provide far better accuracy over existing approximate methods in determining tail probabilities, with the slightly conservative upper bound protecting against false positives. We illustrate the proposed method in the context of a recently published analysis on transcriptomic profiling performed in blood. We provide a method to determine upper bounds and accurate approximate p-values of the rank product statistic. The proposed algorithm provides an order of magnitude increase in throughput as compared with current approaches and offers the opportunity to explore new application domains with even larger multiple testing issue. The R code is published in one of the Additional files and is available at http://www.ru.nl/publish/pages/726696/rankprodbounds.zip .

  12. Characterization of groundwater quality using water evaluation indices, multivariate statistics and geostatistics in central Bangladesh

    Directory of Open Access Journals (Sweden)

    Md. Bodrud-Doza

    2016-04-01

    Full Text Available This study investigates the groundwater quality in the Faridpur district of central Bangladesh based on preselected 60 sample points. Water evaluation indices and a number of statistical approaches such as multivariate statistics and geostatistics are applied to characterize water quality, which is a major factor for controlling the groundwater quality in term of drinking purposes. The study reveal that EC, TDS, Ca2+, total As and Fe values of groundwater samples exceeded Bangladesh and international standards. Ground water quality index (GWQI exhibited that about 47% of the samples were belonging to good quality water for drinking purposes. The heavy metal pollution index (HPI, degree of contamination (Cd, heavy metal evaluation index (HEI reveal that most of the samples belong to low level of pollution. However, Cd provide better alternative than other indices. Principle component analysis (PCA suggests that groundwater quality is mainly related to geogenic (rock–water interaction and anthropogenic source (agrogenic and domestic sewage in the study area. Subsequently, the findings of cluster analysis (CA and correlation matrix (CM are also consistent with the PCA results. The spatial distributions of groundwater quality parameters are determined by geostatistical modeling. The exponential semivariagram model is validated as the best fitted models for most of the indices values. It is expected that outcomes of the study will provide insights for decision makers taking proper measures for groundwater quality management in central Bangladesh.

  13. Identification of Chemical Attribution Signatures of Fentanyl Syntheses Using Multivariate Statistical Analysis of Orthogonal Analytical Data

    Energy Technology Data Exchange (ETDEWEB)

    Mayer, B. P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Mew, D. A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); DeHope, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Spackman, P. E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Williams, A. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-09-24

    Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. The results of these studies can yield detailed information on method of manufacture, starting material source, and final product - all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (GC-MS and LCMS/ MS-TOF) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. This work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.

  14. Multivariate spatial Gaussian mixture modeling for statistical clustering of hemodynamic parameters in functional MRI

    International Nuclear Information System (INIS)

    Fouque, A.L.; Ciuciu, Ph.; Risser, L.; Fouque, A.L.; Ciuciu, Ph.; Risser, L.

    2009-01-01

    In this paper, a novel statistical parcellation of intra-subject functional MRI (fMRI) data is proposed. The key idea is to identify functionally homogenous regions of interest from their hemodynamic parameters. To this end, a non-parametric voxel-based estimation of hemodynamic response function is performed as a prerequisite. Then, the extracted hemodynamic features are entered as the input data of a Multivariate Spatial Gaussian Mixture Model (MSGMM) to be fitted. The goal of the spatial aspect is to favor the recovery of connected components in the mixture. Our statistical clustering approach is original in the sense that it extends existing works done on univariate spatially regularized Gaussian mixtures. A specific Gibbs sampler is derived to account for different covariance structures in the feature space. On realistic artificial fMRI datasets, it is shown that our algorithm is helpful for identifying a parsimonious functional parcellation required in the context of joint detection estimation of brain activity. This allows us to overcome the classical assumption of spatial stationarity of the BOLD signal model. (authors)

  15. A statistical approach to rank multiple priorities in Environmental Epidemiology: an example from high-risk areas in Sardinia, Italy

    Directory of Open Access Journals (Sweden)

    Dolores Catelan

    2008-11-01

    Full Text Available In Environmental Epidemiology, long lists of relative risk estimates from exposed populations are compared to a reference to scrutinize the dataset for extremes. Here, inference on disease profiles for given areas, or for fixed disease population signatures, are of interest and summaries can be obtained averaging over areas or diseases. We have developed a multivariate hierarchical Bayesian approach to estimate posterior rank distributions and we show how to produce league tables of ranks with credibility intervals useful to address the above mentioned inferential problems. Applying the procedure to a real dataset from the report “Environment and Health in Sardinia (Italy” we selected 18 areas characterized by high environmental pressure for industrial, mining or military activities investigated for 29 causes of deaths among male residents. Ranking diseases highlighted the increased burdens of neoplastic (cancerous, and non-neoplastic respiratory diseases in the heavily polluted area of Portoscuso. The averaged ranks by disease over areas showed lung cancer among the three highest positions.

  16. UNCOVERING THE FORMATION OF ULTRACOMPACT DWARF GALAXIES BY MULTIVARIATE STATISTICAL ANALYSIS

    International Nuclear Information System (INIS)

    Chattopadhyay, Tanuka; Sharina, Margarita; Davoust, Emmanuel; De, Tuli; Chattopadhyay, Asis Kumar

    2012-01-01

    We present a statistical analysis of the properties of a large sample of dynamically hot old stellar systems, from globular clusters (GCs) to giant ellipticals, which was performed in order to investigate the origin of ultracompact dwarf galaxies (UCDs). The data were mostly drawn from Forbes et al. We recalculated some of the effective radii, computed mean surface brightnesses and mass-to-light ratios, and estimated ages and metallicities. We completed the sample with GCs of M31. We used a multivariate statistical technique (K-Means clustering), together with a new algorithm (Gap Statistics) for finding the optimum number of homogeneous sub-groups in the sample, using a total of six parameters (absolute magnitude, effective radius, virial mass-to-light ratio, stellar mass-to-light ratio, and metallicity). We found six groups. FK1 and FK5 are composed of high- and low-mass elliptical galaxies, respectively. FK3 and FK6 are composed of high-metallicity and low-metallicity objects, respectively, and both include GCs and UCDs. Two very small groups, FK2 and FK4, are composed of Local Group dwarf spheroidals. Our groups differ in their mean masses and virial mass-to-light ratios. The relations between these two parameters are also different for the various groups. The probability density distributions of metallicity for the four groups of galaxies are similar to those of the GCs and UCDs. The brightest low-metallicity GCs and UCDs tend to follow the mass-metallicity relation like elliptical galaxies. The objects of FK3 are more metal-rich per unit effective luminosity density than high-mass ellipticals.

  17. UNCOVERING THE FORMATION OF ULTRACOMPACT DWARF GALAXIES BY MULTIVARIATE STATISTICAL ANALYSIS

    Energy Technology Data Exchange (ETDEWEB)

    Chattopadhyay, Tanuka [Department of Applied Mathematics, Calcutta University, 92 A.P.C. Road, Calcutta 700009 (India); Sharina, Margarita [Special Astrophysical Observatory, Russian Academy of Sciences, N. Arkhyz, KCh R 369167 (Russian Federation); Davoust, Emmanuel [IRAP, Universite de Toulouse, CNRS, 14 Avenue Edouard Belin, 31400 Toulouse (France); De, Tuli; Chattopadhyay, Asis Kumar, E-mail: tanuka@iucaa.ernet.in, E-mail: sme@sao.ru, E-mail: davoust@ast.obs-mip.fr, E-mail: akcstat@caluniv.ac.in [Department of Statistics, Calcutta University, 35 B.C. Road, Calcutta 700019 (India)

    2012-05-10

    We present a statistical analysis of the properties of a large sample of dynamically hot old stellar systems, from globular clusters (GCs) to giant ellipticals, which was performed in order to investigate the origin of ultracompact dwarf galaxies (UCDs). The data were mostly drawn from Forbes et al. We recalculated some of the effective radii, computed mean surface brightnesses and mass-to-light ratios, and estimated ages and metallicities. We completed the sample with GCs of M31. We used a multivariate statistical technique (K-Means clustering), together with a new algorithm (Gap Statistics) for finding the optimum number of homogeneous sub-groups in the sample, using a total of six parameters (absolute magnitude, effective radius, virial mass-to-light ratio, stellar mass-to-light ratio, and metallicity). We found six groups. FK1 and FK5 are composed of high- and low-mass elliptical galaxies, respectively. FK3 and FK6 are composed of high-metallicity and low-metallicity objects, respectively, and both include GCs and UCDs. Two very small groups, FK2 and FK4, are composed of Local Group dwarf spheroidals. Our groups differ in their mean masses and virial mass-to-light ratios. The relations between these two parameters are also different for the various groups. The probability density distributions of metallicity for the four groups of galaxies are similar to those of the GCs and UCDs. The brightest low-metallicity GCs and UCDs tend to follow the mass-metallicity relation like elliptical galaxies. The objects of FK3 are more metal-rich per unit effective luminosity density than high-mass ellipticals.

  18. Assessment of arsenic and heavy metal contents in cockles (Anadara granosa) using multivariate statistical techniques

    International Nuclear Information System (INIS)

    Abbas Alkarkhi, F.M.; Ismail, Norli; Easa, Azhar Mat

    2008-01-01

    Cockles (Anadara granosa) sample obtained from two rivers in the Penang State of Malaysia were analyzed for the content of arsenic (As) and heavy metals (Cr, Cd, Zn, Cu, Pb, and Hg) using a graphite flame atomic absorption spectrometer (GF-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometer (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data. MANOVA showed a strong significant difference between the two rivers in term of As and heavy metals contents in cockles. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used only two parameters (Zn and Cd) affording more than 72% correct assignations. Results indicated that the two rivers were different in terms of As and heavy metal contents in cockle, and the major difference was due to the contribution of Zn and Cd. A positive correlation was found between discriminate functions (DF) and Zn, Cd and Cr, whereas negative correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metals and arsenic content. Taking into account of these results, it can be suggested that a continuous monitoring of As and heavy metals in cockles be performed in these two rivers

  19. Comparative Estimation of Russia’s Regions Investment Potential on the Base of the Multivariate Statistical Analysis

    Directory of Open Access Journals (Sweden)

    Victor V. Nikitin

    2013-01-01

    Full Text Available The article introduces the algorithm of Russia’s regions investment potential estimation, developed by means of multivariate statistical methods, determines the factors, reflecting regions investment state. The integral indicator was developed on their basis, using statistical data. The article presents regions’ classification on the basis of the integral index

  20. Permutational distribution of the log-rank statistic under random censorship with applications to carcinogenicity assays.

    Science.gov (United States)

    Heimann, G; Neuhaus, G

    1998-03-01

    In the random censorship model, the log-rank test is often used for comparing a control group with different dose groups. If the number of tumors is small, so-called exact methods are often applied for computing critical values from a permutational distribution. Two of these exact methods are discussed and shown to be incorrect. The correct permutational distribution is derived and studied with respect to its behavior under unequal censoring in the light of recent results proving that the permutational version and the unconditional version of the log-rank test are asymptotically equivalent even under unequal censoring. The log-rank test is studied by simulations of a realistic scenario from a bioassay with small numbers of tumors.

  1. Performance evaluation of a hybrid-passive landfill leachate treatment system using multivariate statistical techniques

    Energy Technology Data Exchange (ETDEWEB)

    Wallace, Jack, E-mail: jack.wallace@ce.queensu.ca [Department of Civil Engineering, Queen’s University, Ellis Hall, 58 University Avenue, Kingston, Ontario K7L 3N6 (Canada); Champagne, Pascale, E-mail: champagne@civil.queensu.ca [Department of Civil Engineering, Queen’s University, Ellis Hall, 58 University Avenue, Kingston, Ontario K7L 3N6 (Canada); Monnier, Anne-Charlotte, E-mail: anne-charlotte.monnier@insa-lyon.fr [National Institute for Applied Sciences – Lyon, 20 Avenue Albert Einstein, 69621 Villeurbanne Cedex (France)

    2015-01-15

    Highlights: • Performance of a hybrid passive landfill leachate treatment system was evaluated. • 33 Water chemistry parameters were sampled for 21 months and statistically analyzed. • Parameters were strongly linked and explained most (>40%) of the variation in data. • Alkalinity, ammonia, COD, heavy metals, and iron were criteria for performance. • Eight other parameters were key in modeling system dynamics and criteria. - Abstract: A pilot-scale hybrid-passive treatment system operated at the Merrick Landfill in North Bay, Ontario, Canada, treats municipal landfill leachate and provides for subsequent natural attenuation. Collected leachate is directed to a hybrid-passive treatment system, followed by controlled release to a natural attenuation zone before entering the nearby Little Sturgeon River. The study presents a comprehensive evaluation of the performance of the system using multivariate statistical techniques to determine the interactions between parameters, major pollutants in the leachate, and the biological and chemical processes occurring in the system. Five parameters (ammonia, alkalinity, chemical oxygen demand (COD), “heavy” metals of interest, with atomic weights above calcium, and iron) were set as criteria for the evaluation of system performance based on their toxicity to aquatic ecosystems and importance in treatment with respect to discharge regulations. System data for a full range of water quality parameters over a 21-month period were analyzed using principal components analysis (PCA), as well as principal components (PC) and partial least squares (PLS) regressions. PCA indicated a high degree of association for most parameters with the first PC, which explained a high percentage (>40%) of the variation in the data, suggesting strong statistical relationships among most of the parameters in the system. Regression analyses identified 8 parameters (set as independent variables) that were most frequently retained for modeling

  2. A rank-based algorithm of differential expression analysis for small cell line data with statistical control.

    Science.gov (United States)

    Li, Xiangyu; Cai, Hao; Wang, Xianlong; Ao, Lu; Guo, You; He, Jun; Gu, Yunyan; Qi, Lishuang; Guan, Qingzhou; Lin, Xu; Guo, Zheng

    2017-10-13

    To detect differentially expressed genes (DEGs) in small-scale cell line experiments, usually with only two or three technical replicates for each state, the commonly used statistical methods such as significance analysis of microarrays (SAM), limma and RankProd (RP) lack statistical power, while the fold change method lacks any statistical control. In this study, we demonstrated that the within-sample relative expression orderings (REOs) of gene pairs were highly stable among technical replicates of a cell line but often widely disrupted after certain treatments such like gene knockdown, gene transfection and drug treatment. Based on this finding, we customized the RankComp algorithm, previously designed for individualized differential expression analysis through REO comparison, to identify DEGs with certain statistical control for small-scale cell line data. In both simulated and real data, the new algorithm, named CellComp, exhibited high precision with much higher sensitivity than the original RankComp, SAM, limma and RP methods. Therefore, CellComp provides an efficient tool for analyzing small-scale cell line data. © The Author 2017. Published by Oxford University Press.

  3. Multivariate statistical assessment of heavy metal pollution sources of groundwater around a lead and zinc plant

    Directory of Open Access Journals (Sweden)

    Zamani Abbas Ali

    2012-12-01

    Full Text Available Abstract The contamination of groundwater by heavy metal ions around a lead and zinc plant has been studied. As a case study groundwater contamination in Bonab Industrial Estate (Zanjan-Iran for iron, cobalt, nickel, copper, zinc, cadmium and lead content was investigated using differential pulse polarography (DPP. Although, cobalt, copper and zinc were found correspondingly in 47.8%, 100.0%, and 100.0% of the samples, they did not contain these metals above their maximum contaminant levels (MCLs. Cadmium was detected in 65.2% of the samples and 17.4% of them were polluted by this metal. All samples contained detectable levels of lead and iron with 8.7% and 13.0% of the samples higher than their MCLs. Nickel was also found in 78.3% of the samples, out of which 8.7% were polluted. In general, the results revealed the contamination of groundwater sources in the studied zone. The higher health risks are related to lead, nickel, and cadmium ions. Multivariate statistical techniques were applied for interpreting the experimental data and giving a description for the sources. The data analysis showed correlations and similarities between investigated heavy metals and helps to classify these ion groups. Cluster analysis identified five clusters among the studied heavy metals. Cluster 1 consisted of Pb, Cu, and cluster 3 included Cd, Fe; also each of the elements Zn, Co and Ni was located in groups with single member. The same results were obtained by factor analysis. Statistical investigations revealed that anthropogenic factors and notably lead and zinc plant and pedo-geochemical pollution sources are influencing water quality in the studied area.

  4. Multivariate statistical process control in product quality review assessment - A case study.

    Science.gov (United States)

    Kharbach, M; Cherrah, Y; Vander Heyden, Y; Bouklouze, A

    2017-11-01

    According to the Food and Drug Administration and the European Good Manufacturing Practices (GMP) guidelines, Annual Product Review (APR) is a mandatory requirement in GMP. It consists of evaluating a large collection of qualitative or quantitative data in order to verify the consistency of an existing process. According to the Code of Federal Regulation Part 11 (21 CFR 211.180), all finished products should be reviewed annually for the quality standards to determine the need of any change in specification or manufacturing of drug products. Conventional Statistical Process Control (SPC) evaluates the pharmaceutical production process by examining only the effect of a single factor at the time using a Shewhart's chart. It neglects to take into account the interaction between the variables. In order to overcome this issue, Multivariate Statistical Process Control (MSPC) can be used. Our case study concerns an APR assessment, where 164 historical batches containing six active ingredients, manufactured in Morocco, were collected during one year. Each batch has been checked by assaying the six active ingredients by High Performance Liquid Chromatography according to European Pharmacopoeia monographs. The data matrix was evaluated both by SPC and MSPC. The SPC indicated that all batches are under control, while the MSPC, based on Principal Component Analysis (PCA), for the data being either autoscaled or robust scaled, showed four and seven batches, respectively, out of the Hotelling T 2 95% ellipse. Also, an improvement of the capability of the process is observed without the most extreme batches. The MSPC can be used for monitoring subtle changes in the manufacturing process during an APR assessment. Copyright © 2017 Académie Nationale de Pharmacie. Published by Elsevier Masson SAS. All rights reserved.

  5. Use of multivariate statistics to identify unreliable data obtained using CASA.

    Science.gov (United States)

    Martínez, Luis Becerril; Crispín, Rubén Huerta; Mendoza, Maximino Méndez; Gallegos, Oswaldo Hernández; Martínez, Andrés Aragón

    2013-06-01

    In order to identify unreliable data in a dataset of motility parameters obtained from a pilot study acquired by a veterinarian with experience in boar semen handling, but without experience in the operation of a computer assisted sperm analysis (CASA) system, a multivariate graphical and statistical analysis was performed. Sixteen boar semen samples were aliquoted then incubated with varying concentrations of progesterone from 0 to 3.33 µg/ml and analyzed in a CASA system. After standardization of the data, Chernoff faces were pictured for each measurement, and a principal component analysis (PCA) was used to reduce the dimensionality and pre-process the data before hierarchical clustering. The first twelve individual measurements showed abnormal features when Chernoff faces were drawn. PCA revealed that principal components 1 and 2 explained 63.08% of the variance in the dataset. Values of principal components for each individual measurement of semen samples were mapped to identify differences among treatment or among boars. Twelve individual measurements presented low values of principal component 1. Confidence ellipses on the map of principal components showed no statistically significant effects for treatment or boar. Hierarchical clustering realized on two first principal components produced three clusters. Cluster 1 contained evaluations of the two first samples in each treatment, each one of a different boar. With the exception of one individual measurement, all other measurements in cluster 1 were the same as observed in abnormal Chernoff faces. Unreliable data in cluster 1 are probably related to the operator inexperience with a CASA system. These findings could be used to objectively evaluate the skill level of an operator of a CASA system. This may be particularly useful in the quality control of semen analysis using CASA systems.

  6. Multivariate statistical assessment of heavy metal pollution sources of groundwater around a lead and zinc plant.

    Science.gov (United States)

    Zamani, Abbas Ali; Yaftian, Mohammad Reza; Parizanganeh, Abdolhossein

    2012-12-17

    The contamination of groundwater by heavy metal ions around a lead and zinc plant has been studied. As a case study groundwater contamination in Bonab Industrial Estate (Zanjan-Iran) for iron, cobalt, nickel, copper, zinc, cadmium and lead content was investigated using differential pulse polarography (DPP). Although, cobalt, copper and zinc were found correspondingly in 47.8%, 100.0%, and 100.0% of the samples, they did not contain these metals above their maximum contaminant levels (MCLs). Cadmium was detected in 65.2% of the samples and 17.4% of them were polluted by this metal. All samples contained detectable levels of lead and iron with 8.7% and 13.0% of the samples higher than their MCLs. Nickel was also found in 78.3% of the samples, out of which 8.7% were polluted. In general, the results revealed the contamination of groundwater sources in the studied zone. The higher health risks are related to lead, nickel, and cadmium ions. Multivariate statistical techniques were applied for interpreting the experimental data and giving a description for the sources. The data analysis showed correlations and similarities between investigated heavy metals and helps to classify these ion groups. Cluster analysis identified five clusters among the studied heavy metals. Cluster 1 consisted of Pb, Cu, and cluster 3 included Cd, Fe; also each of the elements Zn, Co and Ni was located in groups with single member. The same results were obtained by factor analysis. Statistical investigations revealed that anthropogenic factors and notably lead and zinc plant and pedo-geochemical pollution sources are influencing water quality in the studied area.

  7. Multivariate statistical analysis of electron energy-loss spectroscopy in anisotropic materials

    International Nuclear Information System (INIS)

    Hu Xuerang; Sun Yuekui; Yuan Jun

    2008-01-01

    Recently, an expression has been developed to take into account the complex dependence of the fine structure in core-level electron energy-loss spectroscopy (EELS) in anisotropic materials on specimen orientation and spectral collection conditions [Y. Sun, J. Yuan, Phys. Rev. B 71 (2005) 125109]. One application of this expression is the development of a phenomenological theory of magic-angle electron energy-loss spectroscopy (MAEELS), which can be used to extract the isotropically averaged spectral information for materials with arbitrary anisotropy. Here we use this expression to extract not only the isotropically averaged spectral information, but also the anisotropic spectral components, without the restriction of MAEELS. The application is based on a multivariate statistical analysis of core-level EELS for anisotropic materials. To demonstrate the applicability of this approach, we have conducted a study on a set of carbon K-edge spectra of multi-wall carbon nanotube (MWCNT) acquired with energy-loss spectroscopic profiling (ELSP) technique and successfully extracted both the averaged and dichroic spectral components of the wrapped graphite-like sheets. Our result shows that this can be a practical alternative to MAEELS for the study of electronic structure of anisotropic materials, in particular for those nanostructures made of layered materials

  8. Multivariate statistical study of heavy metal enrichment in sediments of the Pearl River Estuary

    International Nuclear Information System (INIS)

    Liu, W.X.; Li, X.D.; Shen, Z.G.; Wang, D.C.; Wai, O.W.H.; Li, Y.S.

    2003-01-01

    Multivariate statistical analysis identified the heavy metal accumulation layers of sediment profiles and showed the various sources of metals in the estuary. - The concentrations and chemical partitioning of heavy metals in the sediment cores of the Pearl River Estuary were studied. Based on Pearson correlation coefficients and principal component analysis results, Al was selected as the concentration normalizer for Pb, while Fe was used as the normalizing element for Co, Cu, Ni and Zn. In each profile, sections with metal concentrations exceeding the upper 95% prediction interval of the linear regression model were regarded as metal enrichment layers. The heavy metal accumulation mainly occurred at sites in the western shallow water areas and east channel, which reflected the hydraulic conditions and influence from riparian anthropogenic activities. Heavy metals in the enrichment sections were evaluated by a sequential extraction method for possible chemical forms in sediments. Since the residual, Fe/Mn oxides and organic/sulfide fractions were dominant geochemical phases in the enriched sections, the bioavailability of heavy metals in sediments was generally low. The 206 Pb/ 207 Pb ratios in the metal-enriched sediment sections also revealed the influence of anthropogenic sources. The spatial distribution of cumulative heavy metals in the sediments suggested that the Zn and Cu mainly originated from point sources, while the Pb probably came from non-point sources in the estuary

  9. Application of Multivariate Statistical Analysis to Biomarkers in Se-Turkey Crude Oils

    Science.gov (United States)

    Gürgey, K.; Canbolat, S.

    2017-11-01

    Twenty-four crude oil samples were collected from the 24 oil fields distributed in different districts of SE-Turkey. API and Sulphur content (%), Stable Carbon Isotope, Gas Chromatography (GC), and Gas Chromatography-Mass Spectrometry (GC-MS) data were used to construct a geochemical data matrix. The aim of this study is to examine the genetic grouping or correlations in the crude oil samples, hence the number of source rocks present in the SE-Turkey. To achieve these aims, two of the multivariate statistical analysis techniques (Principle Component Analysis [PCA] and Cluster Analysis were applied to data matrix of 24 samples and 8 source specific biomarker variables/parameters. The results showed that there are 3 genetically different oil groups: Batman-Nusaybin Oils, Adıyaman-Kozluk Oils and Diyarbakir Oils, in addition to a one mixed group. These groupings imply that at least, three different source rocks are present in South-Eastern (SE) Turkey. Grouping of the crude oil samples appears to be consistent with the geographic locations of the oils fields, subsurface stratigraphy as well as geology of the area.

  10. APPLICATION OF MULTIVARIATE STATISTICAL ANALYSIS TO BIOMARKERS IN SE-TURKEY CRUDE OILS

    Directory of Open Access Journals (Sweden)

    K. Gürgey

    2017-11-01

    Full Text Available Twenty-four crude oil samples were collected from the 24 oil fields distributed in different districts of SE-Turkey. API and Sulphur content (%, Stable Carbon Isotope, Gas Chromatography (GC, and Gas Chromatography-Mass Spectrometry (GC-MS data were used to construct a geochemical data matrix. The aim of this study is to examine the genetic grouping or correlations in the crude oil samples, hence the number of source rocks present in the SE-Turkey. To achieve these aims, two of the multivariate statistical analysis techniques (Principle Component Analysis [PCA] and Cluster Analysis were applied to data matrix of 24 samples and 8 source specific biomarker variables/parameters. The results showed that there are 3 genetically different oil groups: Batman-Nusaybin Oils, Adıyaman-Kozluk Oils and Diyarbakir Oils, in addition to a one mixed group. These groupings imply that at least, three different source rocks are present in South-Eastern (SE Turkey. Grouping of the crude oil samples appears to be consistent with the geographic locations of the oils fields, subsurface stratigraphy as well as geology of the area.

  11. Determination of geographic provenance of cotton fibres using multi-isotope profiles and multivariate statistical analysis

    Science.gov (United States)

    Daeid, N. Nic; Meier-Augenstein, W.; Kemp, H. F.

    2012-04-01

    The analysis of cotton fibres can be particularly challenging within a forensic science context where discrimination of one fibre from another is of importance. Normally cotton fibre analysis examines the morphological structure of the recovered material and compares this with that of a known fibre from a particular source of interest. However, the conventional microscopic and chemical analysis of fibres and any associated dyes is generally unsuccessful because of the similar morphology of the fibres. Analysis of the dyes which may have been applied to the cotton fibre can also be undertaken though this can be difficult and unproductive in terms of discriminating one fibre from another. In the study presented here we have explored the potential for Isotope Ratio Mass Spectrometry (IRMS) to be utilised as an additional tool for cotton fibre analysis in an attempt to reveal further discriminatory information. This work has concentrated on un-dyed cotton fibres of known origin in order to expose the potential of the analytical technique. We report the results of a pilot study aimed at testing the hypothesis that multi-element stable isotope analysis of cotton fibres in conjunction with multivariate statistical analysis of the resulting isotopic abundance data using well established chemometric techniques permits sample provenancing based on the determination of where the cotton was grown and as such will facilitate sample discrimination. To date there is no recorded literature of this type of application of IRMS to cotton samples, which may be of forensic science relevance.

  12. Assessment of Reservoir Water Quality Using Multivariate Statistical Techniques: A Case Study of Qiandao Lake, China

    Directory of Open Access Journals (Sweden)

    Qing Gu

    2016-03-01

    Full Text Available Qiandao Lake (Xin’an Jiang reservoir plays a significant role in drinking water supply for eastern China, and it is an attractive tourist destination. Three multivariate statistical methods were comprehensively applied to assess the spatial and temporal variations in water quality as well as potential pollution sources in Qiandao Lake. Data sets of nine parameters from 12 monitoring sites during 2010–2013 were obtained for analysis. Cluster analysis (CA was applied to classify the 12 sampling sites into three groups (Groups A, B and C and the 12 monitoring months into two clusters (April-July, and the remaining months. Discriminant analysis (DA identified Secchi disc depth, dissolved oxygen, permanganate index and total phosphorus as the significant variables for distinguishing variations of different years, with 79.9% correct assignments. Dissolved oxygen, pH and chlorophyll-a were determined to discriminate between the two sampling periods classified by CA, with 87.8% correct assignments. For spatial variation, DA identified Secchi disc depth and ammonia nitrogen as the significant discriminating parameters, with 81.6% correct assignments. Principal component analysis (PCA identified organic pollution, nutrient pollution, domestic sewage, and agricultural and surface runoff as the primary pollution sources, explaining 84.58%, 81.61% and 78.68% of the total variance in Groups A, B and C, respectively. These results demonstrate the effectiveness of integrated use of CA, DA and PCA for reservoir water quality evaluation and could assist managers in improving water resources management.

  13. Confidence limits for contribution plots in multivariate statistical process control using bootstrap estimates.

    Science.gov (United States)

    Babamoradi, Hamid; van den Berg, Frans; Rinnan, Åsmund

    2016-02-18

    In Multivariate Statistical Process Control, when a fault is expected or detected in the process, contribution plots are essential for operators and optimization engineers in identifying those process variables that were affected by or might be the cause of the fault. The traditional way of interpreting a contribution plot is to examine the largest contributing process variables as the most probable faulty ones. This might result in false readings purely due to the differences in natural variation, measurement uncertainties, etc. It is more reasonable to compare variable contributions for new process runs with historical results achieved under Normal Operating Conditions, where confidence limits for contribution plots estimated from training data are used to judge new production runs. Asymptotic methods cannot provide confidence limits for contribution plots, leaving re-sampling methods as the only option. We suggest bootstrap re-sampling to build confidence limits for all contribution plots in online PCA-based MSPC. The new strategy to estimate CLs is compared to the previously reported CLs for contribution plots. An industrial batch process dataset was used to illustrate the concepts. Copyright © 2016 Elsevier B.V. All rights reserved.

  14. Rapid thyroid dysfunction screening based on serum surface-enhanced Raman scattering and multivariate statistical analysis

    Science.gov (United States)

    Tian, Dayong; Lü, Guodong; Zhai, Zhengang; Du, Guoli; Mo, Jiaqing; Lü, Xiaoyi

    2018-01-01

    In this paper, serum surface-enhanced Raman scattering and multivariate statistical analysis are used to investigate a rapid screening technique for thyroid function diseases. At present, the detection of thyroid function has become increasingly important, and it is urgently necessary to develop a rapid and portable method for the detection of thyroid function. Our experimental results show that, by using the Silmeco-based enhanced Raman signal, the signal strength greatly increases and the characteristic peak appears obviously. It is also observed that the Raman spectra of normal and anomalous thyroid function human serum are significantly different. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was used to diagnose thyroid dysfunction, and the diagnostic accuracy was 87.4%. The use of serum surface-enhanced Raman scattering technology combined with PCA-LDA shows good diagnostic performance for the rapid detection of thyroid function. By means of Raman technology, it is expected that a portable device for the rapid detection of thyroid function will be developed.

  15. Application of Multivariate Statistical Analysis in Evaluation of Surface River Water Quality of a Tropical River

    Directory of Open Access Journals (Sweden)

    Teck-Yee Ling

    2017-01-01

    Full Text Available The present study evaluated the spatial variations of surface water quality in a tropical river using multivariate statistical techniques, including cluster analysis (CA and principal component analysis (PCA. Twenty physicochemical parameters were measured at 30 stations along the Batang Baram and its tributaries. The water quality of the Batang Baram was categorized as “slightly polluted” where the chemical oxygen demand and total suspended solids were the most deteriorated parameters. The CA grouped the 30 stations into four clusters which shared similar characteristics within the same cluster, representing the upstream, middle, and downstream regions of the main river and the tributaries from the middle to downstream regions of the river. The PCA has determined a reduced number of six principal components that explained 83.6% of the data set variance. The first PC indicated that the total suspended solids, turbidity, and hydrogen sulphide were the dominant polluting factors which is attributed to the logging activities, followed by the five-day biochemical oxygen demand, total phosphorus, organic nitrogen, and nitrate-nitrogen in the second PC which are related to the discharges from domestic wastewater. The components also imply that logging activities are the major anthropogenic activities responsible for water quality variations in the Batang Baram when compared to the domestic wastewater discharge.

  16. Application of a Multivariate Statistical Technique to Interpreting Data from Multichannel Equipment for the Example of the KLEM Spectrometer

    International Nuclear Information System (INIS)

    Podorozhnyi, D.M.; Postnikov, E.B.; Sveshnikova, L.G.; Turundaevsky, A.N.

    2005-01-01

    A multivariate statistical procedure for solving problems of estimating physical parameters on the basis of data from measurements with multichannel equipment is described. Within the multivariate procedure, an algorithm is constructed for estimating the energy of primary cosmic rays and the exponent in their power-law spectrum. They are investigated by using the KLEM spectrometer (NUCLEON project) as a specific example of measuring equipment. The results of computer experiments simulating the operation of the multivariate procedure for this equipment are given, the proposed approach being compared in these experiments with the one-parameter approach presently used in data processing

  17. Evaluation of significantly modified water bodies in Vojvodina by using multivariate statistical techniques

    Directory of Open Access Journals (Sweden)

    Vujović Svetlana R.

    2013-01-01

    Full Text Available This paper illustrates the utility of multivariate statistical techniques for analysis and interpretation of water quality data sets and identification of pollution sources/factors with a view to get better information about the water quality and design of monitoring network for effective management of water resources. Multivariate statistical techniques, such as factor analysis (FA/principal component analysis (PCA and cluster analysis (CA, were applied for the evaluation of variations and for the interpretation of a water quality data set of the natural water bodies obtained during 2010 year of monitoring of 13 parameters at 33 different sites. FA/PCA attempts to explain the correlations between the observations in terms of the underlying factors, which are not directly observable. Factor analysis is applied to physico-chemical parameters of natural water bodies with the aim classification and data summation as well as segmentation of heterogeneous data sets into smaller homogeneous subsets. Factor loadings were categorized as strong and moderate corresponding to the absolute loading values of >0.75, 0.75-0.50, respectively. Four principal factors were obtained with Eigenvalues >1 summing more than 78 % of the total variance in the water data sets, which is adequate to give good prior information regarding data structure. Each factor that is significantly related to specific variables represents a different dimension of water quality. The first factor F1 accounting for 28 % of the total variance and represents the hydrochemical dimension of water quality. The second factor F2 accounting for 18% of the total variance and may be taken factor of water eutrophication. The third factor F3 accounting 17 % of the total variance and represents the influence of point sources of pollution on water quality. The fourth factor F4 accounting 13 % of the total variance and may be taken as an ecological dimension of water quality. Cluster analysis (CA is an

  18. Solution identification and quantitative analysis of fiber-capacitive drop analyzer based on multivariate statistical methods

    Science.gov (United States)

    Chen, Zhe; Qiu, Zurong; Huo, Xinming; Fan, Yuming; Li, Xinghua

    2017-03-01

    A fiber-capacitive drop analyzer is an instrument which monitors a growing droplet to produce a capacitive opto-tensiotrace (COT). Each COT is an integration of fiber light intensity signals and capacitance signals and can reflect the unique physicochemical property of a liquid. In this study, we propose a solution analytical and concentration quantitative method based on multivariate statistical methods. Eight characteristic values are extracted from each COT. A series of COT characteristic values of training solutions at different concentrations compose a data library of this kind of solution. A two-stage linear discriminant analysis is applied to analyze different solution libraries and establish discriminant functions. Test solutions can be discriminated by these functions. After determining the variety of test solutions, Spearman correlation test and principal components analysis are used to filter and reduce dimensions of eight characteristic values, producing a new representative parameter. A cubic spline interpolation function is built between the parameters and concentrations, based on which we can calculate the concentration of the test solution. Methanol, ethanol, n-propanol, and saline solutions are taken as experimental subjects in this paper. For each solution, nine or ten different concentrations are chosen to be the standard library, and the other two concentrations compose the test group. By using the methods mentioned above, all eight test solutions are correctly identified and the average relative error of quantitative analysis is 1.11%. The method proposed is feasible which enlarges the applicable scope of recognizing liquids based on the COT and improves the concentration quantitative precision, as well.

  19. Multivariate statistical analysis software technologies for astrophysical research involving large data bases

    Science.gov (United States)

    Djorgovski, S. George

    1994-01-01

    We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results.

  20. Determination of dominant biogeochemical processes in a contaminated aquifer-wetland system using multivariate statistical analysis

    Science.gov (United States)

    Baez-Cazull, S. E.; McGuire, J.T.; Cozzarelli, I.M.; Voytek, M.A.

    2008-01-01

    Determining the processes governing aqueous biogeochemistry in a wetland hydrologically linked to an underlying contaminated aquifer is challenging due to the complex exchange between the systems and their distinct responses to changes in precipitation, recharge, and biological activities. To evaluate temporal and spatial processes in the wetland-aquifer system, water samples were collected using cm-scale multichambered passive diffusion samplers (peepers) to span the wetland-aquifer interface over a period of 3 yr. Samples were analyzed for major cations and anions, methane, and a suite of organic acids resulting in a large dataset of over 8000 points, which was evaluated using multivariate statistics. Principal component analysis (PCA) was chosen with the purpose of exploring the sources of variation in the dataset to expose related variables and provide insight into the biogeochemical processes that control the water chemistry of the system. Factor scores computed from PCA were mapped by date and depth. Patterns observed suggest that (i) fermentation is the process controlling the greatest variability in the dataset and it peaks in May; (ii) iron and sulfate reduction were the dominant terminal electron-accepting processes in the system and were associated with fermentation but had more complex seasonal variability than fermentation; (iii) methanogenesis was also important and associated with bacterial utilization of minerals as a source of electron acceptors (e.g., barite BaSO4); and (iv) seasonal hydrological patterns (wet and dry periods) control the availability of electron acceptors through the reoxidation of reduced iron-sulfur species enhancing iron and sulfate reduction. Copyright ?? 2008 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

  1. Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

    Science.gov (United States)

    Djorgovski, S. G.

    1994-01-01

    We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has

  2. Correlation analysis of energy indicators for sustainable development using multivariate statistical techniques

    International Nuclear Information System (INIS)

    Carneiro, Alvaro Luiz Guimaraes; Santos, Francisco Carlos Barbosa dos

    2007-01-01

    Energy is an essential input for social development and economic growth. The production and use of energy cause environmental degradation at all levels, being local, regional and global such as, combustion of fossil fuels causing air pollution; hydropower often causes environmental damage due to the submergence of large areas of land; and global climate change associated with the increasing concentration of greenhouse gases in the atmosphere. As mentioned in chapter 9 of Agenda 21, the Energy is essential to economic and social development and improved quality of life. Much of the world's energy, however, is currently produced and consumed in ways that could not be sustained if technologies were remain constant and if overall quantities were to increase substantially. All energy sources will need to be used in ways that respect the atmosphere, human health, and the environment as a whole. The energy in the context of sustainable development needs a set of quantifiable parameters, called indicators, to measure and monitor important changes and significant progress towards the achievement of the objectives of sustainable development policies. The indicators are divided into four dimensions: social, economic, environmental and institutional. This paper shows a methodology of analysis using Multivariate Statistical Technique that provide the ability to analyse complex sets of data. The main goal of this study is to explore the correlation analysis among the indicators. The data used on this research work, is an excerpt of IBGE (Instituto Brasileiro de Geografia e Estatistica) data census. The core indicators used in this study follows The IAEA (International Atomic Energy Agency) framework: Energy Indicators for Sustainable Development. (author)

  3. Application of multivariate statistical technique for hydrogeochemical assessment of groundwater within the Lower Pra Basin, Ghana

    Science.gov (United States)

    Tay, C. K.; Hayford, E. K.; Hodgson, I. O. A.

    2017-06-01

    Multivariate statistical technique and hydrogeochemical approach were employed for groundwater assessment within the Lower Pra Basin. The main objective was to delineate the main processes that are responsible for the water chemistry and pollution of groundwater within the basin. Fifty-four (54) (No) boreholes were sampled in January 2012 for quality assessment. PCA using Varimax with Kaiser Normalization method of extraction for both rotated space and component matrix have been applied to the data. Results show that Spearman's correlation matrix of major ions revealed expected process-based relationships derived mainly from the geochemical processes, such as ion-exchange and silicate/aluminosilicate weathering within the aquifer. Three main principal components influence the water chemistry and pollution of groundwater within the basin. The three principal components have accounted for approximately 79% of the total variance in the hydrochemical data. Component 1 delineates the main natural processes (water-soil-rock interactions) through which groundwater within the basin acquires its chemical characteristics, Component 2 delineates the incongruent dissolution of silicate/aluminosilicates, while Component 3 delineates the prevalence of pollution principally from agricultural input as well as trace metal mobilization in groundwater within the basin. The loadings and score plots of the first two PCs show grouping pattern which indicates the strength of the mutual relation among the hydrochemical variables. In terms of proper management and development of groundwater within the basin, communities, where intense agriculture is taking place, should be monitored and protected from agricultural activities. especially where inorganic fertilizers are used by creating buffer zones. Monitoring of the water quality especially the water pH is recommended to ensure the acid neutralizing potential of groundwater within the basin thereby, curtailing further trace metal

  4. Authigenic oxide Neodymium Isotopic composition as a proxy of seawater: applying multivariate statistical analyses.

    Science.gov (United States)

    McKinley, C. C.; Scudder, R.; Thomas, D. J.

    2016-12-01

    The Neodymium Isotopic composition (Nd IC) of oxide coatings has been applied as a tracer of water mass composition and used to address fundamental questions about past ocean conditions. The leached authigenic oxide coating from marine sediment is widely assumed to reflect the dissolved trace metal composition of the bottom water interacting with sediment at the seafloor. However, recent studies have shown that readily reducible sediment components, in addition to trace metal fluxes from the pore water, are incorporated into the bottom water, influencing the trace metal composition of leached oxide coatings. This challenges the prevailing application of the authigenic oxide Nd IC as a proxy of seawater composition. Therefore, it is important to identify the component end-members that create sediments of different lithology and determine if, or how they might contribute to the Nd IC of oxide coatings. To investigate lithologic influence on the results of sequential leaching, we selected two sites with complete bulk sediment statistical characterization. Site U1370 in the South Pacific Gyre, is predominantly composed of Rhyolite ( 60%) and has a distinguishable ( 10%) Fe-Mn Oxyhydroxide component (Dunlea et al., 2015). Site 1149 near the Izu-Bonin-Arc is predominantly composed of dispersed ash ( 20-50%) and eolian dust from Asia ( 50-80%) (Scudder et al., 2014). We perform a two-step leaching procedure: a 14 mL of 0.02 M hydroxylamine hydrochloride (HH) in 20% acetic acid buffered to a pH 4 for one hour, targeting metals bound to Fe- and Mn- oxides fractions, and a second HH leach for 12 hours, designed to remove any remaining oxides from the residual component. We analyze all three resulting fractions for a large suite of major, trace and rare earth elements, a sub-set of the samples are also analyzed for Nd IC. We use multivariate statistical analyses of the resulting geochemical data to identify how each component of the sediment partitions across the sequential

  5. Comparison of multivariate and univariate statistical process control and monitoring methods

    International Nuclear Information System (INIS)

    Leger, R.P.; Garland, WM.J.; Macgregor, J.F.

    1996-01-01

    Work in recent years has lead to the development of multivariate process monitoring schemes which use Principal Component Analysis (PCA). This research compares the performance of a univariate scheme and a multivariate PCA scheme used for monitoring a simple process with 11 measured variables. The multivariate PCA scheme was able to adequately represent the process using two principal components. This resulted in a PCA monitoring scheme which used two charts as opposed to 11 charts for the univariate scheme and therefore had distinct advantages in terms of both data representation, presentation, and fault diagnosis capabilities. (author)

  6. A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses

    OpenAIRE

    Buttigieg, Pier Luigi; Ramette, Alban Nicolas

    2014-01-01

    The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynami...

  7. Comparative urine analysis by liquid chromatography-mass spectrometry and multivariate statistics : Method development, evaluation, and application to proteinuria

    NARCIS (Netherlands)

    Kemperman, Ramses F. J.; Horvatovich, Peter L.; Hoekman, Berend; Reijmers, Theo H.; Muskiet, Frits A. J.; Bischoff, Rainer

    2007-01-01

    We describe a platform for the comparative profiling of urine using reversed-phase liquid chromatography-mass spectrometry (LC-MS) and multivariate statistical data analysis. Urinary compounds were separated by gradient elution and subsequently detected by electrospray Ion-Trap MS. The lower limit

  8. Multivariate mixed linear model analysis of longitudinal data: an information-rich statistical technique for analyzing disease resistance data

    Science.gov (United States)

    The mixed linear model (MLM) is currently among the most advanced and flexible statistical modeling techniques and its use in tackling problems in plant pathology has begun surfacing in the literature. The longitudinal MLM is a multivariate extension that handles repeatedly measured data, such as r...

  9. Source Identification of Heavy Metals in Soils Surrounding the Zanjan Zinc Town by Multivariate Statistical Techniques

    Directory of Open Access Journals (Sweden)

    M.A. Delavar

    2016-02-01

    Full Text Available Introduction: The accumulation of heavy metals (HMs in the soil is of increasing concern due to food safety issues, potential health risks, and the detrimental effects on soil ecosystems. HMs may be considered as the most important soil pollutants, because they are not biodegradable and their physical movement through the soil profile is relatively limited. Therefore, root uptake process may provide a big chance for these pollutants to transfer from the surface soil to natural and cultivated plants, which may eventually steer them to human bodies. The general behavior of HMs in the environment, especially their bioavailability in the soil, is influenced by their origin. Hence, source apportionment of HMs may provide some essential information for better management of polluted soils to restrict the HMs entrance to the human food chain. This paper explores the applicability of multivariate statistical techniques in the identification of probable sources that can control the concentration and distribution of selected HMs in the soils surrounding the Zanjan Zinc Specialized Industrial Town (briefly Zinc Town. Materials and Methods: The area under investigation has a size of approximately 4000 ha.It is located around the Zinc Town, Zanjan province. A regular grid sampling pattern with an interval of 500 meters was applied to identify the sample location, and 184 topsoil samples (0-10 cm were collected. The soil samples were air-dried and sieved through a 2 mm polyethylene sieve and then, were digested using HNO3. The total concentrations of zinc (Zn, lead (Pb, cadmium (Cd, Nickel (Ni and copper (Cu in the soil solutions were determined via Atomic Absorption Spectroscopy (AAS. Data were statistically analyzed using the SPSS software version 17.0 for Windows. Correlation Matrix (CM, Principal Component Analyses (PCA and Factor Analyses (FA techniques were performed in order to identify the probable sources of HMs in the studied soils. Results and

  10. Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review

    International Nuclear Information System (INIS)

    Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

    2017-01-01

    Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0–0.10 m, or 0–0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component

  11. Assessment of roadside surface water quality of Savar, Dhaka, Bangladesh using GIS and multivariate statistical techniques

    Science.gov (United States)

    Ahmed, Fahad; Fakhruddin, A. N. M.; Imam, MD. Toufick; Khan, Nasima; Abdullah, Abu Tareq Mohammad; Khan, Tanzir Ahmed; Rahman, Md. Mahfuzur; Uddin, Mohammad Nashir

    2017-11-01

    In this study, multivariate statistical techniques in collaboration with GIS are used to assess the roadside surface water quality of Savar region. Nineteen water samples were collected in dry season and 15 water quality parameters including TSS, TDS, pH, DO, BOD, Cl-, F-, NO3 2-, NO2 -, SO4 2-, Ca, Mg, K, Zn and Pb were measured. The univariate overview of water quality parameters are TSS 25.154 ± 8.674 mg/l, TDS 840.400 ± 311.081 mg/l, pH 7.574 ± 0.256 pH unit, DO 4.544 ± 0.933 mg/l, BOD 0.758 ± 0.179 mg/l, Cl- 51.494 ± 28.095 mg/l, F- 0.771 ± 0.153 mg/l, NO3 2- 2.211 ± 0.878 mg/l, NO2 - 4.692 ± 5.971 mg/l, SO4 2- 69.545 ± 53.873 mg/l, Ca 48.458 ± 22.690 mg/l, Mg 19.676 ± 7.361 mg/l, K 12.874 ± 11.382 mg/l, Zn 0.027 ± 0.029 mg/l, Pb 0.096 ± 0.154 mg/l. The water quality data were subjected to R-mode PCA which resulted in five major components. PC1 explains 28% of total variance and indicates the roadside and brick field dust settle down (TDS, TSS) in the nearby water body. PC2 explains 22.123% of total variance and indicates the agricultural influence (K, Ca, and NO2 -). PC3 describes the contribution of nonpoint pollution from agricultural and soil erosion processes (SO4 2-, Cl-, and K). PC4 depicts heavy positively loaded by vehicle emission and diffusion from battery stores (Zn, Pb). PC5 depicts strong positive loading of BOD and strong negative loading of pH. Cluster analysis represents three major clusters for both water parameters and sampling sites. The site based on cluster showed similar grouping pattern of R-mode factor score map. The present work reveals a new scope to monitor the roadside water quality for future research in Bangladesh.

  12. Study of groundwater arsenic pollution in Lanyang Plain using multivariate statistical analysis

    Science.gov (United States)

    chan, S.

    2013-12-01

    The study area, Lanyang Plain in the eastern Taiwan, has highly developed agriculture and aquaculture, which consume over 70% of the water supplies. Groundwater is frequently considered as an alternative water source. However, the serious arsenic pollution of groundwater in Lanyan Plain should be well studied to ensure the safety of groundwater usage. In this study, 39 groundwater samples were collected. The results of hydrochemistry demonstrate two major trends in Piper diagram. The major trend with most of groundwater samples is determined with water type between Ca+Mg-HCO3 and Na+K-HCO3. This can be explained with cation exchange reaction. The minor trend is obviously corresponding to seawater intrusion, which has water type of Na+K-Cl, because the localities of these samples are all in the coastal area. The multivariate statistical analysis on hydrochemical data was conducted for further exploration on the mechanism of arsenic contamination. Two major factors can be extracted with factor analysis. The major factor includes Ca, Mg and Sr while the minor factor includes Na, K and As. This reconfirms that cation exchange reaction mainly control the groundwater hydrochemistry in the study area. It is worth to note that arsenic is positively related to Na and K. The result of cluster analysis shows that groundwater samples with high arsenic concentration can be grouped into that with high Na, K and HCO3. This supports that cation exchange would enhance the release of arsenic and exclude the effect of seawater intrusion. In other words, the water-rock reaction time is key to obtain higher arsenic content. In general, the major source of arsenic in sediments include exchangeable, reducible and oxidizable phases, which are adsorbed ions, Fe-Mn oxides and organic matters/pyrite, respectively. However, the results of factor analysis do not show apparent correlation between arsenic and Fe/Mn. This may exclude Fe-Mn oxides as a major source of arsenic. The other sources

  13. Hydrochemical analysis of groundwater using multivariate statistical methods - The Volta region, Ghana

    Science.gov (United States)

    Banoeng-Yakubo, B.; Yidana, S.M.; Nti, E.

    2009-01-01

    Q and R-mode multivariate statistical analyses were applied to groundwater chemical data from boreholes and wells in the northern section of the Volta region Ghana. The objective was to determine the processes that affect the hydrochemistry and the variation of these processes in space among the three main geological terrains: the Buem formation, Voltaian System and the Togo series that underlie the area. The analyses revealed three zones in the groundwater flow system: recharge, intermediate and discharge regions. All three zones are clearly different with respect to all the major chemical parameters, with concentrations increasing from the perceived recharge areas through the intermediate regions to the discharge areas. R-mode HCA and factor analysis (using varimax rotation and Kaiser Criterion) were then applied to determine the significant sources of variation in the hydrochemistry. This study finds that groundwater hydrochemistry in the area is controlled by the weathering of silicate and carbonate minerals, as well as the chemistry of infiltrating precipitation. This study finds that the ??D and ??18O data from the area fall along the Global Meteoric Water Line (GMWL). An equation of regression derived for the relationship between ??D and ??18O bears very close semblance to the equation which describes the GMWL. On the basis of this, groundwater in the study area is probably meteoric and fresh. The apparently low salinities and sodicities of the groundwater seem to support this interpretation. The suitability of groundwater for domestic and irrigation purposes is related to its source, which determines its constitution. A plot of the sodium adsorption ratio (SAR) and salinity (EC) data on a semilog axis, suggests that groundwater serves good irrigation quality in the area. Sixty percent (60%), 20% and 20% of the 67 data points used in this study fall within the medium salinity - low sodicity (C2-S1), low salinity -low sodicity (C1-S1) and high salinity - low

  14. Designing a risk-based surveillance program for Mycobacterium avium ssp. paratuberculosis in Norwegian dairy herds using multivariate statistical process control analysis.

    Science.gov (United States)

    Whist, A C; Liland, K H; Jonsson, M E; Sæbø, S; Sviland, S; Østerås, O; Norström, M; Hopp, P

    2014-11-01

    Surveillance programs for animal diseases are critical to early disease detection and risk estimation and to documenting a population's disease status at a given time. The aim of this study was to describe a risk-based surveillance program for detecting Mycobacterium avium ssp. paratuberculosis (MAP) infection in Norwegian dairy cattle. The included risk factors for detecting MAP were purchase of cattle, combined cattle and goat farming, and location of the cattle farm in counties containing goats with MAP. The risk indicators included production data [culling of animals >3 yr of age, carcass conformation of animals >3 yr of age, milk production decrease in older lactating cows (lactations 3, 4, and 5)], and clinical data (diarrhea, enteritis, or both, in animals >3 yr of age). Except for combined cattle and goat farming and cattle farm location, all data were collected at the cow level and summarized at the herd level. Predefined risk factors and risk indicators were extracted from different national databases and combined in a multivariate statistical process control to obtain a risk assessment for each herd. The ordinary Hotelling's T(2) statistic was applied as a multivariate, standardized measure of difference between the current observed state and the average state of the risk factors for a given herd. To make the analysis more robust and adapt it to the slowly developing nature of MAP, monthly risk calculations were based on data accumulated during a 24-mo period. Monitoring of these variables was performed to identify outliers that may indicate deviance in one or more of the underlying processes. The highest-ranked herds were scattered all over Norway and clustered in high-density dairy cattle farm areas. The resulting rankings of herds are being used in the national surveillance program for MAP in 2014 to increase the sensitivity of the ongoing surveillance program in which 5 fecal samples for bacteriological examination are collected from 25 dairy herds

  15. Batch-to-batch quality consistency evaluation of botanical drug products using multivariate statistical analysis of the chromatographic fingerprint.

    Science.gov (United States)

    Xiong, Haoshu; Yu, Lawrence X; Qu, Haibin

    2013-06-01

    Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many kinds of industrial products. In this paper, the combined use of multivariate statistical analysis and chromatographic fingerprinting is presented here to evaluate batch-to-batch quality consistency of botanical drug products. A typical botanical drug product in China, Shenmai injection, was selected as the example to demonstrate the feasibility of this approach. The high-performance liquid chromatographic fingerprint data of historical batches were collected from a traditional Chinese medicine manufacturing factory. Characteristic peaks were weighted by their variability among production batches. A principal component analysis model was established after outliers were modified or removed. Multivariate (Hotelling T(2) and DModX) control charts were finally successfully applied to evaluate the quality consistency. The results suggest useful applications for a combination of multivariate statistical analysis with chromatographic fingerprinting in batch-to-batch quality consistency evaluation for the manufacture of botanical drug products.

  16. Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic.

    Science.gov (United States)

    McArtor, Daniel B; Lubke, Gitta H; Bergeman, C S

    2017-12-01

    Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains.

  17. Reduced Rank Regression

    DEFF Research Database (Denmark)

    Johansen, Søren

    2008-01-01

    The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...

  18. Multivariate statistical study with a factor analysis of foraminiferal fauna from the Chilka Lake, India

    Digital Repository Service at National Institute of Oceanography (India)

    Jayalakshmy, K.V.; Rao, K.K.

    Harbour, En- gland: a reappraisal using multivariate tech- niques. J. Paleontol., 43 (3) : 660-675. Imbrie, J. and F.B. Phleger. 1963. Analisis por vectores de los foraminiferos bentonicos del area de San Diego, California. Soc. Geol. Mex., Bol., 26...

  19. Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

    Science.gov (United States)

    Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome. Chave

    2014-01-01

    We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

  20. Performances of non-parametric statistics in sensitivity analysis and parameter ranking

    International Nuclear Information System (INIS)

    Saltelli, A.

    1987-01-01

    Twelve parametric and non-parametric sensitivity analysis techniques are compared in the case of non-linear model responses. The test models used are taken from the long-term risk analysis for the disposal of high level radioactive waste in a geological formation. They describe the transport of radionuclides through a set of engineered and natural barriers from the repository to the biosphere and to man. The output data from these models are the dose rates affecting the maximum exposed individual of a critical group at a given point in time. All the techniques are applied to the output from the same Monte Carlo simulations, where a modified version of Latin Hypercube method is used for the sample selection. Hypothesis testing is systematically applied to quantify the degree of confidence in the results given by the various sensitivity estimators. The estimators are ranked according to their robustness and stability, on the basis of two test cases. The conclusions are that no estimator can be considered the best from all points of view and recommend the use of more than just one estimator in sensitivity analysis

  1. Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

    Science.gov (United States)

    Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

    2017-12-01

    Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis

  2. Understanding gendered aspects of migration aspiration and motives of university students by multivariate statistical methods

    Directory of Open Access Journals (Sweden)

    Đula Borozan

    2014-03-01

    Full Text Available The paper deals with the application of multivariate analysis of variance and logistic regression in measuring, explaining and evaluating (i gender differences in expressing migration aspirations, and (ii a gender effect on migration motivation of university students in Croatia. The results supported the thesis that migration is a complex gendering process that assumes subjective assessment of the whole set of interrelated motives. According to logistic regression, gender is a significant predictor of migration aspirations among the selected demographic and socio-economic variables. A multivariate analysis of variance showed that gender and migration aspirations in interaction matter when it comes to migration motives, particularly related to the perceived importance of social networks. Females, and especially those who aspire to migrate, assessed these motives as more important than males.

  3. Application of multivariate statistical techniques for differentiation of ripe banana flour based on the composition of elements.

    Science.gov (United States)

    Alkarkhi, Abbas F M; Ramli, Saifullah Bin; Easa, Azhar Mat

    2009-01-01

    Major (sodium, potassium, calcium, magnesium) and minor elements (iron, copper, zinc, manganese) and one heavy metal (lead) of Cavendish banana flour and Dream banana flour were determined, and data were analyzed using multivariate statistical techniques of factor analysis and discriminant analysis. Factor analysis yielded four factors explaining more than 81% of the total variance: the first factor explained 28.73%, comprising magnesium, sodium, and iron; the second factor explained 21.47%, comprising only manganese and copper; the third factor explained 15.66%, comprising zinc and lead; while the fourth factor explained 15.50%, comprising potassium. Discriminant analysis showed that magnesium and sodium exhibited a strong contribution in discriminating the two types of banana flour, affording 100% correct assignation. This study presents the usefulness of multivariate statistical techniques for analysis and interpretation of complex mineral content data from banana flour of different varieties.

  4. A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

    Science.gov (United States)

    Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

    2016-01-13

    A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

  5. Multivariate-Statistical Assessment of Heavy Metals for Agricultural Soils in Northern China

    OpenAIRE

    Yang, Pingguo; Yang, Miao; Mao, Renzhao; Shao, Hongbo

    2014-01-01

    The study evaluated eight heavy metals content and soil pollution from agricultural soils in northern China. Multivariate and geostatistical analysis approaches were used to determine the anthropogenic and natural contribution of soil heavy metal concentrations. Single pollution index and integrated pollution index could be used to evaluate soil heavy metal risk. The results show that the first factor explains 27.3% of the eight soil heavy metals with strong positive loadings on Cu, Zn, and C...

  6. Batch-to-Batch Quality Consistency Evaluation of Botanical Drug Products Using Multivariate Statistical Analysis of the Chromatographic Fingerprint

    OpenAIRE

    Xiong, Haoshu; Yu, Lawrence X.; Qu, Haibin

    2013-01-01

    Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many ...

  7. Resemblance profiles as clustering decision criteria: Estimating statistical power, error, and correspondence for a hypothesis test for multivariate structure.

    Science.gov (United States)

    Kilborn, Joshua P; Jones, David L; Peebles, Ernst B; Naar, David F

    2017-04-01

    Clustering data continues to be a highly active area of data analysis, and resemblance profiles are being incorporated into ecological methodologies as a hypothesis testing-based approach to clustering multivariate data. However, these new clustering techniques have not been rigorously tested to determine the performance variability based on the algorithm's assumptions or any underlying data structures. Here, we use simulation studies to estimate the statistical error rates for the hypothesis test for multivariate structure based on dissimilarity profiles (DISPROF). We concurrently tested a widely used algorithm that employs the unweighted pair group method with arithmetic mean (UPGMA) to estimate the proficiency of clustering with DISPROF as a decision criterion. We simulated unstructured multivariate data from different probability distributions with increasing numbers of objects and descriptors, and grouped data with increasing overlap, overdispersion for ecological data, and correlation among descriptors within groups. Using simulated data, we measured the resolution and correspondence of clustering solutions achieved by DISPROF with UPGMA against the reference grouping partitions used to simulate the structured test datasets. Our results highlight the dynamic interactions between dataset dimensionality, group overlap, and the properties of the descriptors within a group (i.e., overdispersion or correlation structure) that are relevant to resemblance profiles as a clustering criterion for multivariate data. These methods are particularly useful for multivariate ecological datasets that benefit from distance-based statistical analyses. We propose guidelines for using DISPROF as a clustering decision tool that will help future users avoid potential pitfalls during the application of methods and the interpretation of results.

  8. Application of multivariate statistical methods in analyzing expectation surveys in Central Bank of Nigeria

    OpenAIRE

    Raymond, Ogbuka Obinna

    2017-01-01

    In analyzing survey data, most researchers and analysts make use of statistical methods with straight forward statistical approaches. More common, is the use of one‐way, two‐way or multi‐way tables, and graphical displays such as bar charts, line charts, etc. A brief overview of these approaches and a good discussion on aspects needing attention during the data analysis process can be found in Wilson & Stern (2001). In most cases however, analysis procedures that go beyond simp...

  9. Fault detection of a spur gear using vibration signal with multivariable statistical parameters

    Directory of Open Access Journals (Sweden)

    Songpon Klinchaeam

    2014-10-01

    Full Text Available This paper presents a condition monitoring technique of a spur gear fault detection using vibration signal analysis based on time domain. Vibration signals were acquired from gearboxes and used to simulate various faults on spur gear tooth. In this study, vibration signals were applied to monitor a normal and various fault conditions of a spur gear such as normal, scuffing defect, crack defect and broken tooth. The statistical parameters of vibration signal were used to compare and evaluate the value of fault condition. This technique can be applied to set alarm limit of the signal condition based on statistical parameter such as variance, kurtosis, rms and crest factor. These parameters can be used to set as a boundary decision of signal condition. From the results, the vibration signal analysis with single statistical parameter is unclear to predict fault of the spur gears. The using at least two statistical parameters can be clearly used to separate in every case of fault detection. The boundary decision of statistical parameter with the 99.7% certainty ( 3   from 300 referenced dataset and detected the testing condition with 99.7% ( 3   accuracy and had an error of less than 0.3 % using 50 testing dataset.

  10. The outlier sample effects on multivariate statistical data processing geochemical stream sediment survey (Moghangegh region, North West of Iran)

    International Nuclear Information System (INIS)

    Ghanbari, Y.; Habibnia, A.; Memar, A.

    2009-01-01

    In geochemical stream sediment surveys in Moghangegh Region in north west of Iran, sheet 1:50,000, 152 samples were collected and after the analyze and processing of data, it revealed that Yb, Sc, Ni, Li, Eu, Cd, Co, as contents in one sample is far higher than other samples. After detecting this sample as an outlier sample, the effect of this sample on multivariate statistical data processing for destructive effects of outlier sample in geochemical exploration was investigated. Pearson and Spear man correlation coefficient methods and cluster analysis were used for multivariate studies and the scatter plot of some elements together the regression profiles are given in case of 152 and 151 samples and the results are compared. After investigation of multivariate statistical data processing results, it was realized that results of existence of outlier samples may appear as the following relations between elements: - true relation between two elements, which have no outlier frequency in the outlier sample. - false relation between two elements which one of them has outlier frequency in the outlier sample. - complete false relation between two elements which both have outlier frequency in the outlier sample

  11. Southeast Atlantic Cloud Properties in a Multivariate Statistical Model - How Relevant is Air Mass History for Local Cloud Properties?

    Science.gov (United States)

    Fuchs, Julia; Cermak, Jan; Andersen, Hendrik

    2017-04-01

    This study aims at untangling the impacts of external dynamics and local conditions on cloud properties in the Southeast Atlantic (SEA) by combining satellite and reanalysis data using multivariate statistics. The understanding of clouds and their determinants at different scales is important for constraining the Earth's radiative budget, and thus prominent in climate-system research. In this study, SEA stratocumulus cloud properties are observed not only as the result of local environmental conditions but also as affected by external dynamics and spatial origins of air masses entering the study area. In order to assess to what extent cloud properties are impacted by aerosol concentration, air mass history, and meteorology, a multivariate approach is conducted using satellite observations of aerosol and cloud properties (MODIS, SEVIRI), information on aerosol species composition (MACC) and meteorological context (ERA-Interim reanalysis). To account for the often-neglected but important role of air mass origin, information on air mass history based on HYSPLIT modeling is included in the statistical model. This multivariate approach is intended to lead to a better understanding of the physical processes behind observed stratocumulus cloud properties in the SEA.

  12. Analysis of fatty acid composition of sea cucumber Apostichopus japonicus using multivariate statistics

    Science.gov (United States)

    Xu, Qinzeng; Gao, Fei; Xu, Qiang; Yang, Hongsheng

    2014-11-01

    Fatty acids (FAs) provide energy and also can be used to trace trophic relationships among organisms. Sea cucumber Apostichopus japonicus goes into a state of aestivation during warm summer months. We examined fatty acid profiles in aestivated and non-aestivated A. japonicus using multivariate analyses (PERMANOVA, MDS, ANOSIM, and SIMPER). The results indicate that the fatty acid profiles of aestivated and non-aestivated sea cucumbers differed significantly. The FAs that were produced by bacteria and brown kelp contributed the most to the differences in the fatty acid composition of aestivated and nonaestivated sea cucumbers. Aestivated sea cucumbers may synthesize FAs from heterotrophic bacteria during early aestivation, and long chain FAs such as eicosapentaenoic (EPA) and docosahexaenoic acid (DHA) that produced from intestinal degradation, are digested during deep aestivation. Specific changes in the fatty acid composition of A. japonicus during aestivation needs more detailed study in the future.

  13. Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India).

    Science.gov (United States)

    Mavukkandy, Musthafa Odayooth; Karmakar, Subhankar; Harikumar, P S

    2014-09-01

    The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. The effectiveness of existing river water quality monitoring

  14. Improved statistical signal detection in pharmacovigilance by combining multiple strength-of-evidence aspects in vigiRank.

    Science.gov (United States)

    Caster, Ola; Juhlin, Kristina; Watson, Sarah; Norén, G Niklas

    2014-08-01

    Detection of unknown risks with marketed medicines is key to securing the optimal care of individual patients and to reducing the societal burden from adverse drug reactions. Large collections of individual case reports remain the primary source of information and require effective analytics to guide clinical assessors towards likely drug safety signals. Disproportionality analysis is based solely on aggregate numbers of reports and naively disregards report quality and content. However, these latter features are the very fundament of the ensuing clinical assessment. Our objective was to develop and evaluate a data-driven screening algorithm for emerging drug safety signals that accounts for report quality and content. vigiRank is a predictive model for emerging safety signals, here implemented with shrinkage logistic regression to identify predictive variables and estimate their respective contributions. The variables considered for inclusion capture different aspects of strength of evidence, including quality and clinical content of individual reports, as well as trends in time and geographic spread. A reference set of 264 positive controls (historical safety signals from 2003 to 2007) and 5,280 negative controls (pairs of drugs and adverse events not listed in the Summary of Product Characteristics of that drug in 2012) was used for model fitting and evaluation; the latter used fivefold cross-validation to protect against over-fitting. All analyses were performed on a reconstructed version of VigiBase(®) as of 31 December 2004, at around which time most safety signals in our reference set were emerging. The following aspects of strength of evidence were selected for inclusion into vigiRank: the numbers of informative and recent reports, respectively; disproportional reporting; the number of reports with free-text descriptions of the case; and the geographic spread of reporting. vigiRank offered a statistically significant improvement in area under the receiver

  15. Multivariate statistical approximation of the in situ gamma-ray spectrometry of the State of Zacatecas, Mexico

    International Nuclear Information System (INIS)

    Lopez I, J. F.; Rios M, C.; Mireles G, F.; Saucedo A, S.; Davila R, I.; Pinedo, J.L.

    2017-09-01

    The environmental radioactivity evaluation is a key point in the assessment of the environmental quality. Through this, it can be found possible radioactive contamination, locate possible Uranium and Thorium deposits and evaluate the primordial isotopes concentration due to human activities. A radioactive map of the Zacatecas State, Mexico is under construction based on in situ gamma-ray spectrometry. The present work reports the results of the multivariate statistical approximation of the measured activity data. Based on Pearson correlation, the 228 Ac and 208 Tl activities are statistically significant, while the 214 Bi and 214 Pb activities are not statistically significant. These can be due to the existence or not of secular equilibrium in the Thorium and Uranium series. (Author)

  16. Multivariate statistical approximation of the in situ gamma-ray spectrometry of the State of Zacatecas, Mexico

    Energy Technology Data Exchange (ETDEWEB)

    Lopez I, J. F.; Rios M, C.; Mireles G, F.; Saucedo A, S.; Davila R, I.; Pinedo, J.L., E-mail: fernandolf498@gmail.com [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Cipres No. 10, Fracc. La Penuela, 98060 Zacatecas, Zac. (Mexico)

    2017-09-15

    The environmental radioactivity evaluation is a key point in the assessment of the environmental quality. Through this, it can be found possible radioactive contamination, locate possible Uranium and Thorium deposits and evaluate the primordial isotopes concentration due to human activities. A radioactive map of the Zacatecas State, Mexico is under construction based on in situ gamma-ray spectrometry. The present work reports the results of the multivariate statistical approximation of the measured activity data. Based on Pearson correlation, the {sup 228}Ac and {sup 208}Tl activities are statistically significant, while the {sup 214}Bi and {sup 214}Pb activities are not statistically significant. These can be due to the existence or not of secular equilibrium in the Thorium and Uranium series. (Author)

  17. [Monitoring method of extraction process for Schisandrae Chinensis Fructus based on near infrared spectroscopy and multivariate statistical process control].

    Science.gov (United States)

    Xu, Min; Zhang, Lei; Yue, Hong-Shui; Pang, Hong-Wei; Ye, Zheng-Liang; Ding, Li

    2017-10-01

    To establish an on-line monitoring method for extraction process of Schisandrae Chinensis Fructus, the formula medicinal material of Yiqi Fumai lyophilized injection by combining near infrared spectroscopy with multi-variable data analysis technology. The multivariate statistical process control (MSPC) model was established based on 5 normal batches in production and 2 test batches were monitored by PC scores, DModX and Hotelling T2 control charts. The results showed that MSPC model had a good monitoring ability for the extraction process. The application of the MSPC model to actual production process could effectively achieve on-line monitoring for extraction process of Schisandrae Chinensis Fructus, and can reflect the change of material properties in the production process in real time. This established process monitoring method could provide reference for the application of process analysis technology in the process quality control of traditional Chinese medicine injections. Copyright© by the Chinese Pharmaceutical Association.

  18. Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods.

    Science.gov (United States)

    Li, Jinling; He, Ming; Han, Wei; Gu, Yifan

    2009-05-30

    An investigation on heavy metal sources, i.e., Cu, Zn, Ni, Pb, Cr, and Cd in the coastal soils of Shanghai, China, was conducted using multivariate statistical methods (principal component analysis, clustering analysis, and correlation analysis). All the results of the multivariate analysis showed that: (i) Cu, Ni, Pb, and Cd had anthropogenic sources (e.g., overuse of chemical fertilizers and pesticides, industrial and municipal discharges, animal wastes, sewage irrigation, etc.); (ii) Zn and Cr were associated with parent materials and therefore had natural sources (e.g., the weathering process of parent materials and subsequent pedo-genesis due to the alluvial deposits). The effect of heavy metals in the soils was greatly affected by soil formation, atmospheric deposition, and human activities. These findings provided essential information on the possible sources of heavy metals, which would contribute to the monitoring and assessment process of agricultural soils in worldwide regions.

  19. An Application of Multivariate Statistical Analysis for Query-Driven Visualization

    Energy Technology Data Exchange (ETDEWEB)

    Gosink, Luke J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Garth, Christoph [Univ. of California, Davis, CA (United States); Anderson, John C. [Univ. of California, Davis, CA (United States); Bethel, E. Wes [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joy, Kenneth I. [Univ. of California, Davis, CA (United States)

    2011-03-01

    Driven by the ability to generate ever-larger, increasingly complex data, there is an urgent need in the scientific community for scalable analysis methods that can rapidly identify salient trends in scientific data. Query-Driven Visualization (QDV) strategies are among the small subset of techniques that can address both large and highly complex datasets. This paper extends the utility of QDV strategies with a statistics-based framework that integrates non-parametric distribution estimation techniques with a new segmentation strategy to visually identify statistically significant trends and features within the solution space of a query. In this framework, query distribution estimates help users to interactively explore their query's solution and visually identify the regions where the combined behavior of constrained variables is most important, statistically, to their inquiry. Our new segmentation strategy extends the distribution estimation analysis by visually conveying the individual importance of each variable to these regions of high statistical significance. We demonstrate the analysis benefits these two strategies provide and show how they may be used to facilitate the refinement of constraints over variables expressed in a user's query. We apply our method to datasets from two different scientific domains to demonstrate its broad applicability.

  20. Flow prediction models using macroclimatic variables and multivariate statistical techniques in the Cauca River Valley

    International Nuclear Information System (INIS)

    Carvajal Escobar Yesid; Munoz, Flor Matilde

    2007-01-01

    The project this centred in the revision of the state of the art of the ocean-atmospheric phenomena that you affect the Colombian hydrology especially The Phenomenon Enos that causes a socioeconomic impact of first order in our country, it has not been sufficiently studied; therefore it is important to approach the thematic one, including the variable macroclimates associated to the Enos in the analyses of water planning. The analyses include revision of statistical techniques of analysis of consistency of hydrological data with the objective of conforming a database of monthly flow of the river reliable and homogeneous Cauca. Statistical methods are used (Analysis of data multivariante) specifically The analysis of principal components to involve them in the development of models of prediction of flows monthly means in the river Cauca involving the Lineal focus as they are the model autoregressive AR, ARX and Armax and the focus non lineal Net Artificial Network.

  1. Bootstrap-based confidence estimation in PCA and multivariate statistical process control

    DEFF Research Database (Denmark)

    Babamoradi, Hamid

    be used to detect outliers in the data since the outliers can distort the bootstrap estimates. Bootstrap-based confidence limits were suggested as alternative to the asymptotic limits for control charts and contribution plots in MSPC (Paper II). The results showed that in case of the Q-statistic......Traditional/Asymptotic confidence estimation has limited applicability since it needs statistical theories to estimate the confidences, which are not available for all indicators/parameters. Furthermore, in case the theories are available for a specific indicator/parameter, the theories are based....... The goal was to improve process monitoring by improving the quality of MSPC charts and contribution plots. Bootstrapping algorithm to build confidence limits was illustrated in a case study format (Paper I). The main steps in the algorithm were discussed where a set of sensible choices (plus...

  2. Combined data preprocessing and multivariate statistical analysis characterizes fed-batch culture of mouse hybridoma cells for rational medium design.

    Science.gov (United States)

    Selvarasu, Suresh; Kim, Do Yun; Karimi, Iftekhar A; Lee, Dong-Yup

    2010-10-01

    We present an integrated framework for characterizing fed-batch cultures of mouse hybridoma cells producing monoclonal antibody (mAb). This framework systematically combines data preprocessing, elemental balancing and statistical analysis technique. Initially, specific rates of cell growth, glucose/amino acid consumptions and mAb/metabolite productions were calculated via curve fitting using logistic equations, with subsequent elemental balancing of the preprocessed data indicating the presence of experimental measurement errors. Multivariate statistical analysis was then employed to understand physiological characteristics of the cellular system. The results from principal component analysis (PCA) revealed three major clusters of amino acids with similar trends in their consumption profiles: (i) arginine, threonine and serine, (ii) glycine, tyrosine, phenylalanine, methionine, histidine and asparagine, and (iii) lysine, valine and isoleucine. Further analysis using partial least square (PLS) regression identified key amino acids which were positively or negatively correlated with the cell growth, mAb production and the generation of lactate and ammonia. Based on these results, the optimal concentrations of key amino acids in the feed medium can be inferred, potentially leading to an increase in cell viability and productivity, as well as a decrease in toxic waste production. The study demonstrated how the current methodological framework using multivariate statistical analysis techniques can serve as a potential tool for deriving rational medium design strategies. Copyright © 2010 Elsevier B.V. All rights reserved.

  3. DETERMINING INDICATORS OF URBAN HOUSEHOLD WATER CONSUMPTION THROUGH MULTIVARIATE STATISTICAL TECHNIQUES

    Directory of Open Access Journals (Sweden)

    Gledsneli Maria Lima Lins

    2010-12-01

    Full Text Available Water has a decisive influence on populations’ life quality – specifically in areas like urban supply, drainage, and effluents treatment – due to its sound impact over public health. Water rational use constitutes the greatest challenge faced by water demand management, mainly with regard to urban household water consumption. This makes it important to develop researches to assist water managers and public policy-makers in planning and formulating water demand measures which may allow urban water rational use to be met. This work utilized the multivariate techniques Factor Analysis and Multiple Linear Regression Analysis – in order to determine the participation level of socioeconomic and climatic variables in monthly urban household consumption changes – applying them to two districts of Campina Grande city (State of Paraíba, Brazil. The districts were chosen based on socioeconomic criterion (income level so as to evaluate their water consumer’s behavior. A 9-year monthly data series (from year 2000 up to 2008 was utilized, comprising family income, water tariff, and quantity of household connections (economies – as socioeconomic variables – and average temperature and precipitation, as climatic variables. For both the selected districts of Campina Grande city, the obtained results point out the variables “water tariff” and “family income” as indicators of these district’s household consumption.

  4. Multivariate statistical techniques for the evaluation of surface water quality of the Himalayan foothills streams, Pakistan

    Science.gov (United States)

    Malik, Riffat Naseem; Hashmi, Muhammad Zaffar

    2017-10-01

    Himalayan foothills streams, Pakistan play an important role in living water supply and irrigation of farmlands; thus, the water quality is closely related to public health. Multivariate techniques were applied to check spatial and seasonal trends, and metals contamination sources of the Himalayan foothills streams, Pakistan. Grab surface water samples were collected from different sites (5-15 cm water depth) in pre-washed polyethylene containers. Fast Sequential Atomic Absorption Spectrophotometer (Varian FSAA-240) was used to measure the metals concentration. Concentrations of Ni, Cu, and Mn were high in pre-monsoon season than the post-monsoon season. Cluster analysis identified impaired, moderately impaired and least impaired clusters based on water parameters. Discriminant function analysis indicated spatial variability in water was due to temperature, electrical conductivity, nitrates, iron and lead whereas seasonal variations were correlated with 16 physicochemical parameters. Factor analysis identified municipal and poultry waste, automobile activities, surface runoff, and soil weathering as major sources of contamination. Levels of Mn, Cr, Fe, Pb, Cd, Zn and alkalinity were above the WHO and USEPA standards for surface water. The results of present study will help to higher authorities for the management of the Himalayan foothills streams.

  5. The iron bars from the ‘Gresham Ship’: employing multivariate statistics to further slag inclusion analysis of ferrous objects

    DEFF Research Database (Denmark)

    Birch, Thomas; Martinón-Torres, Marcos

    2015-01-01

    An assemblage of post-medieval iron bars was found with the Princes Channel wreck, salvaged from the Thames Estuary in 2003. They were recorded and studied, with a focus on metallography and slag inclusion analysis. The investigation provided an opportunity to explore the use of multivariate...... statistical techniques to analyse slag inclusion data. Cluster analysis supplemented by principal components analysis revealed two groups of iron, probably originating from different smelting systems, which were compared to those observed macroscopically and through metallography. The analyses reveal...

  6. A multivariate statistical methodology for detection of degradation and failure trends using nuclear power plant operational data

    International Nuclear Information System (INIS)

    Samanta, P.K.; Teichmann, T.

    1990-01-01

    In this paper, a multivariate statistical method is presented and demonstrated as a means for analyzing nuclear power plant transients (or events) and safety system performance for detection of malfunctions and degradations within the course of the event based on operational data. The study provides the methodology and illustrative examples based on data gathered from simulation of nuclear power plant transients (due to lack of easily accessible operational data). Such an approach, once fully developed, can be used to detect failure trends and patterns and so can lead to prevention of conditions with serious safety implications

  7. Risk management and statistical multivariate analysis approach for design and optimization of satranidazole nanoparticles.

    Science.gov (United States)

    Dhat, Shalaka; Pund, Swati; Kokare, Chandrakant; Sharma, Pankaj; Shrivastava, Birendra

    2017-01-01

    Rapidly evolving technical and regulatory landscapes of the pharmaceutical product development necessitates risk management with application of multivariate analysis using Process Analytical Technology (PAT) and Quality by Design (QbD). Poorly soluble, high dose drug, Satranidazole was optimally nanoprecipitated (SAT-NP) employing principles of Formulation by Design (FbD). The potential risk factors influencing the critical quality attributes (CQA) of SAT-NP were identified using Ishikawa diagram. Plackett-Burman screening design was adopted to screen the eight critical formulation and process parameters influencing the mean particle size, zeta potential and dissolution efficiency at 30min in pH7.4 dissolution medium. Pareto charts (individual and cumulative) revealed three most critical factors influencing CQA of SAT-NP viz. aqueous stabilizer (Polyvinyl alcohol), release modifier (Eudragit® S 100) and volume of aqueous phase. The levels of these three critical formulation attributes were optimized by FbD within established design space to minimize mean particle size, poly dispersity index, and maximize encapsulation efficiency of SAT-NP. Lenth's and Bayesian analysis along with mathematical modeling of results allowed identification and quantification of critical formulation attributes significantly active on the selected CQAs. The optimized SAT-NP exhibited mean particle size; 216nm, polydispersity index; 0.250, zeta potential; -3.75mV and encapsulation efficiency; 78.3%. The product was lyophilized using mannitol to form readily redispersible powder. X-ray diffraction analysis confirmed the conversion of crystalline SAT to amorphous form. In vitro release of SAT-NP in gradually pH changing media showed 95%) in pH7.4 in next 3h, indicative of burst release after a lag time. This investigation demonstrated effective application of risk management and QbD tools in developing site-specific release SAT-NP by nanoprecipitation. Copyright © 2016 Elsevier B.V. All

  8. Drought assessment in the Duero basin (Central Spain) by means of multivariate extreme value statistics

    Science.gov (United States)

    Kallache, M.

    2012-04-01

    Droughts cause important losses. On the Iberian Peninsula, for example, non-irrigated agriculture and the tourism sector are affected in regular intervals. The goal of this study is the description of droughts and their dependence in the Duero basin in Central Spain. To do so, daily or monthly precipitation data is used. Here cumulative precipitation deficits below a threshold define meteorological droughts. This drought indicator is similar to the commonly used standard precipitation index. However, here the focus lies on the modeling of severe droughts, which is done by applying multivariate extreme value theory (MEVT) to model extreme drought events. Data from several stations are assessed jointly, thus the uncertainty of the results is reduced. Droughts are a complex phenomenon, their severity, spatial extension and duration has to be taken into account. Our approach captures severity and spatial extension. In general we find a high correlation between deficit volumes and drought duration, thus the duration is not explicitely modeled. We apply a MEVT model with asymmetric logistic dependence function, which is capable to model asymptotic dependence and independence (cf. Ramos and Ledford, 2009). To summarize the information on the dependence in the joint tail of the extreme drought events, we utilise the fragility index (Geluk et al., 2007). Results show that droughts also occur frequently in winter. Moreover, it is very common for one site to suffer dry conditions, whilst neighboring areas experience normal or even humid conditions. Interpolation is thus difficult. Bivariate extremal dependence is present in the data. However, most stations are at least asymptotically independent. The according fragility indices are important information for risk calculations. The emerging spatial patterns for bivariate dependence are mostly influenced by topography. When looking at the dependence between more than two stations, it shows that joint extremes can occur more

  9. Multivariate Statistical Analysis of Water Chemistry in Evaluating the Origin of Contamination in Many Devils Wash, Shiprock, New Mexico

    International Nuclear Information System (INIS)

    2012-01-01

    This report evaluates the chemistry of seep water occurring in three desert drainages near Shiprock, New Mexico: Many Devils Wash, Salt Creek Wash, and Eagle Nest Arroyo. Through the use of geochemical plotting tools and multivariate statistical analysis techniques, analytical results of samples collected from the three drainages are compared with the groundwater chemistry at a former uranium mill in the Shiprock area (the Shiprock site), managed by the U.S. Department of Energy Office of Legacy Management. The objective of this study was to determine, based on the water chemistry of the samples, if statistically significant patterns or groupings are apparent between the sample populations and, if so, whether there are any reasonable explanations for those groupings.

  10. Multivariate Statistical Analysis of Water Chemistry in Evaluating the Origin of Contamination in Many Devils Wash, Shiprock, New Mexico

    Energy Technology Data Exchange (ETDEWEB)

    None, None

    2012-12-31

    This report evaluates the chemistry of seep water occurring in three desert drainages near Shiprock, New Mexico: Many Devils Wash, Salt Creek Wash, and Eagle Nest Arroyo. Through the use of geochemical plotting tools and multivariate statistical analysis techniques, analytical results of samples collected from the three drainages are compared with the groundwater chemistry at a former uranium mill in the Shiprock area (the Shiprock site), managed by the U.S. Department of Energy Office of Legacy Management. The objective of this study was to determine, based on the water chemistry of the samples, if statistically significant patterns or groupings are apparent between the sample populations and, if so, whether there are any reasonable explanations for those groupings.

  11. Assessment of Near-Bottom Water Quality of Southwestern Coast of Sarawak, Borneo, Malaysia: A Multivariate Statistical Approach

    Directory of Open Access Journals (Sweden)

    Chen-Lin Soo

    2017-01-01

    Full Text Available The study on Sarawak coastal water quality is scarce, not to mention the application of the multivariate statistical approach to investigate the spatial variation of water quality and to identify the pollution source in Sarawak coastal water. Hence, the present study aimed to evaluate the spatial variation of water quality along the coastline of the southwestern region of Sarawak using multivariate statistical techniques. Seventeen physicochemical parameters were measured at 11 stations along the coastline with approximately 225 km length. The coastal water quality showed spatial heterogeneity where the cluster analysis grouped the 11 stations into four different clusters. Deterioration in coastal water quality has been observed in different regions of Sarawak corresponding to land use patterns in the region. Nevertheless, nitrate-nitrogen exceeded the guideline value at all sampling stations along the coastline. The principal component analysis (PCA has determined a reduced number of five principal components that explained 89.0% of the data set variance. The first PC indicated that the nutrients were the dominant polluting factors, which is attributed to the domestic, agricultural, and aquaculture activities, followed by the suspended solids in the second PC which are related to the logging activities.

  12. A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses.

    Science.gov (United States)

    Buttigieg, Pier Luigi; Ramette, Alban

    2014-12-01

    The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynamic, web-based resource providing accessible descriptions of numerous multivariate techniques relevant to microbial ecologists. A combination of interactive elements allows users to discover and navigate between methods relevant to their needs and examine how they have been used by others in the field. We have designed GUSTA ME to become a community-led and -curated service, which we hope will provide a common reference and forum to discuss and disseminate analytical techniques relevant to the microbial ecology community. © 2014 The Authors. FEMS Microbiology Ecology published by John Wiley & Sons Ltd on behalf of Federation of European Microbiological Societies.

  13. Multivariate Statistical Analysis: a tool for groundwater quality assessment in the hidrogeologic region of the Ring of Cenotes, Yucatan, Mexico.

    Science.gov (United States)

    Ye, M.; Pacheco Castro, R. B.; Pacheco Avila, J.; Cabrera Sansores, A.

    2014-12-01

    The karstic aquifer of Yucatan is a vulnerable and complex system. The first fifteen meters of this aquifer have been polluted, due to this the protection of this resource is important because is the only source of potable water of the entire State. Through the assessment of groundwater quality we can gain some knowledge about the main processes governing water chemistry as well as spatial patterns which are important to establish protection zones. In this work multivariate statistical techniques are used to assess the groundwater quality of the supply wells (30 to 40 meters deep) in the hidrogeologic region of the Ring of Cenotes, located in Yucatan, Mexico. Cluster analysis and principal component analysis are applied in groundwater chemistry data of the study area. Results of principal component analysis show that the main sources of variation in the data are due sea water intrusion and the interaction of the water with the carbonate rocks of the system and some pollution processes. The cluster analysis shows that the data can be divided in four clusters. The spatial distribution of the clusters seems to be random, but is consistent with sea water intrusion and pollution with nitrates. The overall results show that multivariate statistical analysis can be successfully applied in the groundwater quality assessment of this karstic aquifer.

  14. Development of infill drilling recovery models for carbonates reservoirs using neural networks and multivariate statistical as a novel method

    International Nuclear Information System (INIS)

    Soto, R; Wu, Ch. H; Bubela, A M

    1999-01-01

    This work introduces a novel methodology to improve reservoir characterization models. In this methodology we integrated multivariate statistical analyses, and neural network models for forecasting the infill drilling ultimate oil recovery from reservoirs in San Andres and Clearfork carbonate formations in west Texas. Development of the oil recovery forecast models help us to understand the relative importance of dominant reservoir characteristics and operational variables, reproduce recoveries for units included in the database, forecast recoveries for possible new units in similar geological setting, and make operational (infill drilling) decisions. The variety of applications demands the creation of multiple recovery forecast models. We have developed intelligent software (Soto, 1998), oilfield intelligence (01), as an engineering tool to improve the characterization of oil and gas reservoirs. 01 integrates neural networks and multivariate statistical analysis. It is composed of five main subsystems: data input, preprocessing, architecture design, graphic design, and inference engine modules. One of the challenges in this research was to identify the dominant and the optimum number of independent variables. The variables include porosity, permeability, water saturation, depth, area, net thickness, gross thickness, formation volume factor, pressure, viscosity, API gravity, number of wells in initial water flooding, number of wells for primary recovery, number of infill wells over the initial water flooding, PRUR, IWUR, and IDUR. Multivariate principal component analysis is used to identify the dominant and the optimum number of independent variables. We compared the results from neural network models with the non-parametric approach. The advantage of the non-parametric regression is that it is easy to use. The disadvantage is that it retains a large variance of forecast results for a particular data set. We also used neural network concepts to develop recovery

  15. Using integrated multivariate statistics to assess the hydrochemistry of surface water quality, Lake Taihu basin, China

    Directory of Open Access Journals (Sweden)

    Xiangyu Mu

    2014-09-01

    Full Text Available Natural factors and anthropogenic activities both contribute dissolved chemical loads to  lakes and streams.  Mineral solubility,  geomorphology of the drainage basin, source strengths and climate all contribute to concentrations and their variability. Urbanization and agriculture waste-water particularly lead to aquatic environmental degradation. Major contaminant sources and controls on water quality can be asssessed by analyzing the variability in proportions of major and minor solutes in water coupled to mutivariate statistical methods.   The demand for freshwater needed for increasing crop production puulation and industrialization occurs almost everywhere in in China and these conflicting needs have led to widespread water contamination. Because of heavy nutrient loadings from all of these sources, Lake Taihu (eastern China notably suffers periodic hyper-eutrophication and drinking water deterioration, which has led to shortages of freshwater for the City of Wuxi and other nearby cities. This lake, the third largest freshwater body in China, has historically beeen considered a cultural treasure of China, and has supported long-term fisheries. The is increasing pressure to remediate the present contamination which compromises both aquiculture and the prior economic base centered on tourism.  However, remediation cannot be effectively done without first characterizing the broad nature of the non-point source pollution. To this end, we investigated the hydrochemical setting of Lake Taihu to determine how different land use types influence the variability of surface water chemistry in different water sources to the lake. We found that waters broadly show wide variability ranging from  calcium-magnesium-bicarbonate hydrochemical facies type to mixed sodium-sulfate-chloride type. Principal components analysis produced three principal components that explained 78% of the variance in the water quality and reflect three major types of water

  16. Damage detection of engine bladed-disks using multivariate statistical analysis

    Science.gov (United States)

    Fang, X.; Tang, J.

    2006-03-01

    The timely detection of damage in aero-engine bladed-disks is an extremely important and challenging research topic. Bladed-disks have high modal density and, particularly, their vibration responses are subject to significant uncertainties due to manufacturing tolerance (blade-to-blade difference or mistuning), operating condition change and sensor noise. In this study, we present a new methodology for the on-line damage detection of engine bladed-disks using their vibratory responses during spin-up or spin-down operations which can be measured by blade-tip-timing sensing technique. We apply a principle component analysis (PCA)-based approach for data compression, feature extraction, and denoising. The non-model based damage detection is achieved by analyzing the change between response features of the healthy structure and of the damaged one. We facilitate such comparison by incorporating the Hotelling's statistic T2 analysis, which yields damage declaration with a given confidence level. The effectiveness of the method is demonstrated by case studies.

  17. Bio hydrogen production from cassava starch by anaerobic mixed cultures: Multivariate statistical modeling

    Science.gov (United States)

    Tien, Hai Minh; Le, Kien Anh; Le, Phung Thi Kim

    2017-09-01

    Bio hydrogen is a sustainable energy resource due to its potentially higher efficiency of conversion to usable power, high energy efficiency and non-polluting nature resource. In this work, the experiments have been carried out to indicate the possibility of generating bio hydrogen as well as identifying effective factors and the optimum conditions from cassava starch. Experimental design was used to investigate the effect of operating temperature (37-43 °C), pH (6-7), and inoculums ratio (6-10 %) to the yield hydrogen production, the COD reduction and the ratio of volume of hydrogen production to COD reduction. The statistical analysis of the experiment indicated that the significant effects for the fermentation yield were the main effect of temperature, pH and inoculums ratio. The interaction effects between them seem not significant. The central composite design showed that the polynomial regression models were in good agreement with the experimental results. This result will be applied to enhance the process of cassava starch processing wastewater treatment.

  18. Multivariate Regression Analysis and Statistical Modeling for Summer Extreme Precipitation over the Yangtze River Basin, China

    Directory of Open Access Journals (Sweden)

    Tao Gao

    2014-01-01

    Full Text Available Extreme precipitation is likely to be one of the most severe meteorological disasters in China; however, studies on the physical factors affecting precipitation extremes and corresponding prediction models are not accurately available. From a new point of view, the sensible heat flux (SHF and latent heat flux (LHF, which have significant impacts on summer extreme rainfall in Yangtze River basin (YRB, have been quantified and then selections of the impact factors are conducted. Firstly, a regional extreme precipitation index was applied to determine Regions of Significant Correlation (RSC by analyzing spatial distribution of correlation coefficients between this index and SHF, LHF, and sea surface temperature (SST on global ocean scale; then the time series of SHF, LHF, and SST in RSCs during 1967–2010 were selected. Furthermore, other factors that significantly affect variations in precipitation extremes over YRB were also selected. The methods of multiple stepwise regression and leave-one-out cross-validation (LOOCV were utilized to analyze and test influencing factors and statistical prediction model. The correlation coefficient between observed regional extreme index and model simulation result is 0.85, with significant level at 99%. This suggested that the forecast skill was acceptable although many aspects of the prediction model should be improved.

  19. Hierarchical probabilistic regionalization of volcanism for Sengan region in Japan using multivariate statistical techniques and geostatistical interpolation techniques

    International Nuclear Information System (INIS)

    Park, Jinyong; Balasingham, P.; McKenna, Sean Andrew; Kulatilake, Pinnaduwa H. S. W.

    2004-01-01

    Sandia National Laboratories, under contract to Nuclear Waste Management Organization of Japan (NUMO), is performing research on regional classification of given sites in Japan with respect to potential volcanic disruption using multivariate statistics and geo-statistical interpolation techniques. This report provides results obtained for hierarchical probabilistic regionalization of volcanism for the Sengan region in Japan by applying multivariate statistical techniques and geostatistical interpolation techniques on the geologic data provided by NUMO. A workshop report produced in September 2003 by Sandia National Laboratories (Arnold et al., 2003) on volcanism lists a set of most important geologic variables as well as some secondary information related to volcanism. Geologic data extracted for the Sengan region in Japan from the data provided by NUMO revealed that data are not available at the same locations for all the important geologic variables. In other words, the geologic variable vectors were found to be incomplete spatially. However, it is necessary to have complete geologic variable vectors to perform multivariate statistical analyses. As a first step towards constructing complete geologic variable vectors, the Universal Transverse Mercator (UTM) zone 54 projected coordinate system and a 1 km square regular grid system were selected. The data available for each geologic variable on a geographic coordinate system were transferred to the aforementioned grid system. Also the recorded data on volcanic activity for Sengan region were produced on the same grid system. Each geologic variable map was compared with the recorded volcanic activity map to determine the geologic variables that are most important for volcanism. In the regionalized classification procedure, this step is known as the variable selection step. The following variables were determined as most important for volcanism: geothermal gradient, groundwater temperature, heat discharge, groundwater

  20. Evaluation of strategies to promote learning using ICT: the case of a course on Topics of Multivariate Statistics

    Directory of Open Access Journals (Sweden)

    Mario Miguel Ojeda Ramírez

    2017-01-01

    Full Text Available Currently some teachers implement different methods in order to promote education linked to reality, to provide more effective training and a meaningful learning. Activemethods aim to increase motivation and create scenarios in which student participation is central to achieve a more meaningful learning. This paper reports on the implementation of a process of educational innovation in the course of Topics of Multivariate Statistics offered in the degree in Statistical Sciences and Techniques at the Universidad Veracruzana (Mexico. The strategies used as sets for data collection, design and project development and realization of individual and group presentations are described. Information and communication technologies (ICT used are: EMINUS, distributed education platform of the Universidad Veracruzana, and managing files with Dropbox, plus communication via WhatsApp. The R software was used for statistical analysis and for making presentations in academic forums. To explore students' perceptions depth interviews were conducted and indicators for evaluating the student satisfaction were defined; the results show positive evidence, concluding that students were satisfied with the way that the course was designed and implemented. They also stated that they feel able to apply what they have learned. The opinions put that using these strategies they were feeling in preparation for their professional life. Finally, some suggestions for improving the course in future editions are included.

  1. Multivariate Statistical Analysis of Orthogonal Mass Spectral Data for the Identification of Chemical Attribution Signatures of 3-Methylfentanyl

    Energy Technology Data Exchange (ETDEWEB)

    Mayer, B. P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Valdez, C. A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); DeHope, A. J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Spackman, P. E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Sanner, R. D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Martinez, H. P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Williams, A. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2016-11-28

    Critical to many modern forensic investigations is the chemical attribution of the origin of an illegal drug. This process greatly relies on identification of compounds indicative of its clandestine or commercial production. The results of these studies can yield detailed information on method of manufacture, sophistication of the synthesis operation, starting material source, and final product. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3- methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods were studied in an effort to identify and classify route-specific signatures. These methods were chosen to minimize the use of scheduled precursors, complicated laboratory equipment, number of overall steps, and demanding reaction conditions. Using gas and liquid chromatographies combined with mass spectrometric methods (GC-QTOF and LC-QTOF) in conjunction with inductivelycoupled plasma mass spectrometry (ICP-MS), over 240 distinct compounds and elements were monitored. As seen in our previous work with CAS of fentanyl synthesis the complexity of the resultant data matrix necessitated the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 62 statistically significant, route-specific CAS were identified. Statistical classification models using a variety of machine learning techniques were then developed with the ability to predict the method of 3-methylfentanyl synthesis from three blind crude samples generated by synthetic chemists without prior experience with these methods.

  2. Study on sources of colored glaze of Xiyue Temple in Shanxi province by INAA and multivariable statistical analysis

    International Nuclear Information System (INIS)

    Cheng Lin; Feng Songlin

    2005-01-01

    The major, minor and trace elements in the bodies of ancient colored glazes which came from the site of Xiyue Temple and Lidipo kiln in Shanxi province, and were unearthed from the stratums of Song, Yuan, Ming, Early Qing and Late Qing dynasty were analyzed by instrumental neutron activation analysis (INAA). The results of multivariable statistical analyses show that the chemical compositions of the colored glaze bodies are steady from Song to Early Qing dynasty, but distinctly different from that in Late Qing. Probably, the sources of fired material of ancient colored glaze from Song to Early Qing came from the site of Xiyue Temple. The chemical compositions of three pieces of colored glazes in Ming dynasty and that in Late Qing are similar to that of Lidipo kiln. From this, authors could conclude that the sources of the materials of ancient coloured glazes of Xiyue Temple in Late Qing dynasty were fired in Lidipo kiln. (authors)

  3. [Methods of the multivariate statistical analysis of so-called polyetiological diseases using the example of coronary heart disease].

    Science.gov (United States)

    Lifshits, A M

    1979-01-01

    General characteristics of the multivariate statistical analysis (MSA) is given. Methodical premises and criteria for the selection of an adequate MSA method applicable to pathoanatomic investigations of the epidemiology of multicausal diseases are presented. The experience of using MSA with computors and standard computing programs in studies of coronary arteries aterosclerosis on the materials of 2060 autopsies is described. The combined use of 4 MSA methods: sequential, correlational, regressional, and discriminant permitted to quantitate the contribution of each of the 8 examined risk factors in the development of aterosclerosis. The most important factors were found to be the age, arterial hypertension, and heredity. Occupational hypodynamia and increased fatness were more important in men, whereas diabetes melitus--in women. The registration of this combination of risk factors by MSA methods provides for more reliable prognosis of the likelihood of coronary heart disease with a fatal outcome than prognosis of the degree of coronary aterosclerosis.

  4. Correlating phospholipid fatty acids (PLFA) in a landfill leachate polluted aquifer with biogeochemical factors by multivariate statistical methods

    DEFF Research Database (Denmark)

    Ludvigsen, Liselotte; Albrechtsen, Hans-Jørgen; Rootzén, Helle

    1997-01-01

    Different multivariate statistical analyses were applied to phospholipid fatty acids representing the biomass composition and to different biogeochemical parameters measured in 37 samples from a landfill contaminated aquifer at Grindsted Landfill (Denmark). Principal component analysis...... and correspondence analysis were used to identify groups of samples showing similar patterns with respect to biogeochemical variables and phospholipid fatty acid composition. The principal component analysis revealed that for the biogeochemical parameters the first principal component was linked to the pollution...... was used to allocate samples of phospholipid fatty acids into predefined classes. A large percentages of samples were classified correctly when discriminating samples into groups of dissolved organic carbon and specific conductivity, indicating that the biomass is highly influenced by the pollution...

  5. Multivariate statistical assessments of greenhouse-gas-induced climatic change and comparison with results from general circulation models

    International Nuclear Information System (INIS)

    Schoenwiese, C.D.

    1990-01-01

    Based on univariate correction and coherence analyses, including techniques moving in time, and taking account of the physical basis of the relationships, a simple multivariate concept is presented which correlates observational climatic time series simultaneously with solar, volcanic, ENSO (El Nino/Souther Oscillation) and anthropogenic greenhouse-gas forcing. The climatic elements considered are air temperature (near the ground and stratosphere), sea surface temperature, sea level and precipitation, and cover at least the period 1881-1980 (stratospheric temperature only since 1960). The climate signal assessments which may be hypothetically attributed to the observed CO 2 or equivalent CO 2 (implying additional greenhouse gases) increase are compared with those resulting from GCM experiments. In case of the Northern hemisphere air temperature these comparisons are performed not only in respect to hemispheric and global means, but also in respect to the regional and seasonal patterns. Autocorrelations and phase shifts of the climate response to natural and anthropogenic forcing complicate the statistical assessments

  6. Compositional differences among Chinese soy sauce types studied by (13)C NMR spectroscopy coupled with multivariate statistical analysis.

    Science.gov (United States)

    Kamal, Ghulam Mustafa; Wang, Xiaohua; Bin Yuan; Wang, Jie; Sun, Peng; Zhang, Xu; Liu, Maili

    2016-09-01

    Soy sauce a well known seasoning all over the world, especially in Asia, is available in global market in a wide range of types based on its purpose and the processing methods. Its composition varies with respect to the fermentation processes and addition of additives, preservatives and flavor enhancers. A comprehensive (1)H NMR based study regarding the metabonomic variations of soy sauce to differentiate among different types of soy sauce available on the global market has been limited due to the complexity of the mixture. In present study, (13)C NMR spectroscopy coupled with multivariate statistical data analysis like principle component analysis (PCA), and orthogonal partial least square-discriminant analysis (OPLS-DA) was applied to investigate metabonomic variations among different types of soy sauce, namely super light, super dark, red cooking and mushroom soy sauce. The main additives in soy sauce like glutamate, sucrose and glucose were easily distinguished and quantified using (13)C NMR spectroscopy which were otherwise difficult to be assigned and quantified due to serious signal overlaps in (1)H NMR spectra. The significantly higher concentration of sucrose in dark, red cooking and mushroom flavored soy sauce can directly be linked to the addition of caramel in soy sauce. Similarly, significantly higher level of glutamate in super light as compared to super dark and mushroom flavored soy sauce may come from the addition of monosodium glutamate. The study highlights the potentiality of (13)C NMR based metabonomics coupled with multivariate statistical data analysis in differentiating between the types of soy sauce on the basis of level of additives, raw materials and fermentation procedures. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Hydrochemical evolution and groundwater flow processes in the Galilee and Eromanga basins, Great Artesian Basin, Australia: a multivariate statistical approach.

    Science.gov (United States)

    Moya, Claudio E; Raiber, Matthias; Taulis, Mauricio; Cox, Malcolm E

    2015-03-01

    The Galilee and Eromanga basins are sub-basins of the Great Artesian Basin (GAB). In this study, a multivariate statistical approach (hierarchical cluster analysis, principal component analysis and factor analysis) is carried out to identify hydrochemical patterns and assess the processes that control hydrochemical evolution within key aquifers of the GAB in these basins. The results of the hydrochemical assessment are integrated into a 3D geological model (previously developed) to support the analysis of spatial patterns of hydrochemistry, and to identify the hydrochemical and hydrological processes that control hydrochemical variability. In this area of the GAB, the hydrochemical evolution of groundwater is dominated by evapotranspiration near the recharge area resulting in a dominance of the Na-Cl water types. This is shown conceptually using two selected cross-sections which represent discrete groundwater flow paths from the recharge areas to the deeper parts of the basins. With increasing distance from the recharge area, a shift towards a dominance of carbonate (e.g. Na-HCO3 water type) has been observed. The assessment of hydrochemical changes along groundwater flow paths highlights how aquifers are separated in some areas, and how mixing between groundwater from different aquifers occurs elsewhere controlled by geological structures, including between GAB aquifers and coal bearing strata of the Galilee Basin. The results of this study suggest that distinct hydrochemical differences can be observed within the previously defined Early Cretaceous-Jurassic aquifer sequence of the GAB. A revision of the two previously recognised hydrochemical sequences is being proposed, resulting in three hydrochemical sequences based on systematic differences in hydrochemistry, salinity and dominant hydrochemical processes. The integrated approach presented in this study which combines different complementary multivariate statistical techniques with a detailed assessment of the

  8. Real-time monitoring of a coffee roasting process with near infrared spectroscopy using multivariate statistical analysis: A feasibility study.

    Science.gov (United States)

    Catelani, Tiago A; Santos, João Rodrigo; Páscoa, Ricardo N M J; Pezza, Leonardo; Pezza, Helena R; Lopes, João A

    2018-03-01

    This work proposes the use of near infrared (NIR) spectroscopy in diffuse reflectance mode and multivariate statistical process control (MSPC) based on principal component analysis (PCA) for real-time monitoring of the coffee roasting process. The main objective was the development of a MSPC methodology able to early detect disturbances to the roasting process resourcing to real-time acquisition of NIR spectra. A total of fifteen roasting batches were defined according to an experimental design to develop the MSPC models. This methodology was tested on a set of five batches where disturbances of different nature were imposed to simulate real faulty situations. Some of these batches were used to optimize the model while the remaining was used to test the methodology. A modelling strategy based on a time sliding window provided the best results in terms of distinguishing batches with and without disturbances, resourcing to typical MSPC charts: Hotelling's T 2 and squared predicted error statistics. A PCA model encompassing a time window of four minutes with three principal components was able to efficiently detect all disturbances assayed. NIR spectroscopy combined with the MSPC approach proved to be an adequate auxiliary tool for coffee roasters to detect faults in a conventional roasting process in real-time. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Multivariate statistical process control of a continuous pharmaceutical twin-screw granulation and fluid bed drying process.

    Science.gov (United States)

    Silva, A F; Sarraguça, M C; Fonteyne, M; Vercruysse, J; De Leersnyder, F; Vanhoorne, V; Bostijn, N; Verstraeten, M; Vervaet, C; Remon, J P; De Beer, T; Lopes, J A

    2017-08-07

    A multivariate statistical process control (MSPC) strategy was developed for the monitoring of the ConsiGma™-25 continuous tablet manufacturing line. Thirty-five logged variables encompassing three major units, being a twin screw high shear granulator, a fluid bed dryer and a product control unit, were used to monitor the process. The MSPC strategy was based on principal component analysis of data acquired under normal operating conditions using a series of four process runs. Runs with imposed disturbances in the dryer air flow and temperature, in the granulator barrel temperature, speed and liquid mass flow and in the powder dosing unit mass flow were utilized to evaluate the model's monitoring performance. The impact of the imposed deviations to the process continuity was also evaluated using Hotelling's T 2 and Q residuals statistics control charts. The influence of the individual process variables was assessed by analyzing contribution plots at specific time points. Results show that the imposed disturbances were all detected in both control charts. Overall, the MSPC strategy was successfully developed and applied. Additionally, deviations not associated with the imposed changes were detected, mainly in the granulator barrel temperature control. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Arsenic health risk assessment in drinking water and source apportionment using multivariate statistical techniques in Kohistan region, northern Pakistan.

    Science.gov (United States)

    Muhammad, Said; Tahir Shah, M; Khan, Sardar

    2010-10-01

    The present study was conducted in Kohistan region, where mafic and ultramafic rocks (Kohistan island arc and Indus suture zone) and metasedimentary rocks (Indian plate) are exposed. Water samples were collected from the springs, streams and Indus river and analyzed for physical parameters, anions, cations and arsenic (As(3+), As(5+) and arsenic total). The water quality in Kohistan region was evaluated by comparing the physio-chemical parameters with permissible limits set by Pakistan environmental protection agency and world health organization. Most of the studied parameters were found within their respective permissible limits. However in some samples, the iron and arsenic concentrations exceeded their permissible limits. For health risk assessment of arsenic, the average daily dose, hazards quotient (HQ) and cancer risk were calculated by using statistical formulas. The values of HQ were found >1 in the samples collected from Jabba, Dubair, while HQ values were pollution load was also calculated by using multivariate statistical techniques like one-way ANOVA, correlation analysis, regression analysis, cluster analysis and principle component analysis. Copyright © 2010 Elsevier Ltd. All rights reserved.

  11. Statistical mixture design and multivariate analysis of inkjet printed a-WO3/TiO2/WOX electrochromic films.

    Science.gov (United States)

    Wojcik, Pawel Jerzy; Pereira, Luís; Martins, Rodrigo; Fortunato, Elvira

    2014-01-13

    An efficient mathematical strategy in the field of solution processed electrochromic (EC) films is outlined as a combination of an experimental work, modeling, and information extraction from massive computational data via statistical software. Design of Experiment (DOE) was used for statistical multivariate analysis and prediction of mixtures through a multiple regression model, as well as the optimization of a five-component sol-gel precursor subjected to complex constraints. This approach significantly reduces the number of experiments to be realized, from 162 in the full factorial (L=3) and 72 in the extreme vertices (D=2) approach down to only 30 runs, while still maintaining a high accuracy of the analysis. By carrying out a finite number of experiments, the empirical modeling in this study shows reasonably good prediction ability in terms of the overall EC performance. An optimized ink formulation was employed in a prototype of a passive EC matrix fabricated in order to test and trial this optically active material system together with a solid-state electrolyte for the prospective application in EC displays. Coupling of DOE with chromogenic material formulation shows the potential to maximize the capabilities of these systems and ensures increased productivity in many potential solution-processed electrochemical applications.

  12. One Hundred Ways to be Non-Fickian - A Rigorous Multi-Variate Statistical Analysis of Pore-Scale Transport

    Science.gov (United States)

    Most, Sebastian; Nowak, Wolfgang; Bijeljic, Branko

    2015-04-01

    Fickian transport in groundwater flow is the exception rather than the rule. Transport in porous media is frequently simulated via particle methods (i.e. particle tracking random walk (PTRW) or continuous time random walk (CTRW)). These methods formulate transport as a stochastic process of particle position increments. At the pore scale, geometry and micro-heterogeneities prohibit the commonly made assumption of independent and normally distributed increments to represent dispersion. Many recent particle methods seek to loosen this assumption. Hence, it is important to get a better understanding of the processes at pore scale. For our analysis we track the positions of 10.000 particles migrating through the pore space over time. The data we use come from micro CT scans of a homogeneous sandstone and encompass about 10 grain sizes. Based on those images we discretize the pore structure and simulate flow at the pore scale based on the Navier-Stokes equation. This flow field realistically describes flow inside the pore space and we do not need to add artificial dispersion during the transport simulation. Next, we use particle tracking random walk and simulate pore-scale transport. Finally, we use the obtained particle trajectories to do a multivariate statistical analysis of the particle motion at the pore scale. Our analysis is based on copulas. Every multivariate joint distribution is a combination of its univariate marginal distributions. The copula represents the dependence structure of those univariate marginals and is therefore useful to observe correlation and non-Gaussian interactions (i.e. non-Fickian transport). The first goal of this analysis is to better understand the validity regions of commonly made assumptions. We are investigating three different transport distances: 1) The distance where the statistical dependence between particle increments can be modelled as an order-one Markov process. This would be the Markovian distance for the process, where

  13. Structures and algorithms for post-processing large data sets and multi-variate functions in spatio-temporal statistics

    KAUST Repository

    Litvinenko, Alexander

    2017-12-10

    Matrices began in the 2nd century BC with the Chinese. One can find traces, which go to the 4th century BC to the Babylonians. The text ``Nine Chapters of the Mathematical Art\\'\\' written during the Han Dynasty in China gave the first known example of matrix methods. They were used to solve simultaneous linear equations (more in http://math.nie.edu.sg/bwjyeo/it/MathsOnline_AM/livemath/the/IT3AMMatricesHistory.html). The first ideas of the maximum likelihood estimation (MLE) was introduces by Laplace (1749-1827), by Gauss (1777-1855), the Likelihood was defined by Thiele Thorvald (1838-1910). Why we still use matrices? The matrix data format is more than 2200 years old. Our world is multi-dimensional! Why not to introduce a more appropriate data format and why not to reformulate the MLE method for it? In this work we are utilizing the low-rank tensor formats for multi-dimansional functions, which appear in spatial statistics.

  14. Evaluation of multivariate statistical analyses for monitoring and prediction of processes in an seawater reverse osmosis desalination plant

    International Nuclear Information System (INIS)

    Kolluri, Srinivas Sahan; Esfahani, Iman Janghorban; Garikiparthy, Prithvi Sai Nadh; Yoo, Chang Kyoo

    2015-01-01

    Our aim was to analyze, monitor, and predict the outcomes of processes in a full-scale seawater reverse osmosis (SWRO) desalination plant using multivariate statistical techniques. Multivariate analysis of variance (MANOVA) was used to investigate the performance and efficiencies of two SWRO processes, namely, pore controllable fiber filterreverse osmosis (PCF-SWRO) and sand filtration-ultra filtration-reverse osmosis (SF-UF-SWRO). Principal component analysis (PCA) was applied to monitor the two SWRO processes. PCA monitoring revealed that the SF-UF-SWRO process could be analyzed reliably with a low number of outliers and disturbances. Partial least squares (PLS) analysis was then conducted to predict which of the seven input parameters of feed flow rate, PCF/SF-UF filtrate flow rate, temperature of feed water, turbidity feed, pH, reverse osmosis (RO)flow rate, and pressure had a significant effect on the outcome variables of permeate flow rate and concentration. Root mean squared errors (RMSEs) of the PLS models for permeate flow rates were 31.5 and 28.6 for the PCF-SWRO process and SF-UF-SWRO process, respectively, while RMSEs of permeate concentrations were 350.44 and 289.4, respectively. These results indicate that the SF-UF-SWRO process can be modeled more accurately than the PCF-SWRO process, because the RMSE values of permeate flowrate and concentration obtained using a PLS regression model of the SF-UF-SWRO process were lower than those obtained for the PCF-SWRO process.

  15. Evaluation of multivariate statistical analyses for monitoring and prediction of processes in an seawater reverse osmosis desalination plant

    Energy Technology Data Exchange (ETDEWEB)

    Kolluri, Srinivas Sahan; Esfahani, Iman Janghorban; Garikiparthy, Prithvi Sai Nadh; Yoo, Chang Kyoo [Kyung Hee University, Yongin (Korea, Republic of)

    2015-08-15

    Our aim was to analyze, monitor, and predict the outcomes of processes in a full-scale seawater reverse osmosis (SWRO) desalination plant using multivariate statistical techniques. Multivariate analysis of variance (MANOVA) was used to investigate the performance and efficiencies of two SWRO processes, namely, pore controllable fiber filterreverse osmosis (PCF-SWRO) and sand filtration-ultra filtration-reverse osmosis (SF-UF-SWRO). Principal component analysis (PCA) was applied to monitor the two SWRO processes. PCA monitoring revealed that the SF-UF-SWRO process could be analyzed reliably with a low number of outliers and disturbances. Partial least squares (PLS) analysis was then conducted to predict which of the seven input parameters of feed flow rate, PCF/SF-UF filtrate flow rate, temperature of feed water, turbidity feed, pH, reverse osmosis (RO)flow rate, and pressure had a significant effect on the outcome variables of permeate flow rate and concentration. Root mean squared errors (RMSEs) of the PLS models for permeate flow rates were 31.5 and 28.6 for the PCF-SWRO process and SF-UF-SWRO process, respectively, while RMSEs of permeate concentrations were 350.44 and 289.4, respectively. These results indicate that the SF-UF-SWRO process can be modeled more accurately than the PCF-SWRO process, because the RMSE values of permeate flowrate and concentration obtained using a PLS regression model of the SF-UF-SWRO process were lower than those obtained for the PCF-SWRO process.

  16. Quantitative profiling of polar metabolites in herbal medicine injections for multivariate statistical evaluation based on independence principal component analysis.

    Directory of Open Access Journals (Sweden)

    Miaomiao Jiang

    Full Text Available Botanical primary metabolites extensively exist in herbal medicine injections (HMIs, but often were ignored to control. With the limitation of bias towards hydrophilic substances, the primary metabolites with strong polarity, such as saccharides, amino acids and organic acids, are usually difficult to detect by the routinely applied reversed-phase chromatographic fingerprint technology. In this study, a proton nuclear magnetic resonance (1H NMR profiling method was developed for efficient identification and quantification of small polar molecules, mostly primary metabolites in HMIs. A commonly used medicine, Danhong injection (DHI, was employed as a model. With the developed method, 23 primary metabolites together with 7 polyphenolic acids were simultaneously identified, of which 13 metabolites with fully separated proton signals were quantified and employed for further multivariate quality control assay. The quantitative 1H NMR method was validated with good linearity, precision, repeatability, stability and accuracy. Based on independence principal component analysis (IPCA, the contents of 13 metabolites were characterized and dimensionally reduced into the first two independence principal components (IPCs. IPC1 and IPC2 were then used to calculate the upper control limits (with 99% confidence ellipsoids of χ2 and Hotelling T2 control charts. Through the constructed upper control limits, the proposed method was successfully applied to 36 batches of DHI to examine the out-of control sample with the perturbed levels of succinate, malonate, glucose, fructose, salvianic acid and protocatechuic aldehyde. The integrated strategy has provided a reliable approach to identify and quantify multiple polar metabolites of DHI in one fingerprinting spectrum, and it has also assisted in the establishment of IPCA models for the multivariate statistical evaluation of HMIs.

  17. Seasonal rationalization of river water quality sampling locations: a comparative study of the modified Sanders and multivariate statistical approaches.

    Science.gov (United States)

    Varekar, Vikas; Karmakar, Subhankar; Jha, Ramakar

    2016-02-01

    The design of surface water quality sampling location is a crucial decision-making process for rationalization of monitoring network. The quantity, quality, and types of available dataset (watershed characteristics and water quality data) may affect the selection of appropriate design methodology. The modified Sanders approach and multivariate statistical techniques [particularly factor analysis (FA)/principal component analysis (PCA)] are well-accepted and widely used techniques for design of sampling locations. However, their performance may vary significantly with quantity, quality, and types of available dataset. In this paper, an attempt has been made to evaluate performance of these techniques by accounting the effect of seasonal variation, under a situation of limited water quality data but extensive watershed characteristics information, as continuous and consistent river water quality data is usually difficult to obtain, whereas watershed information may be made available through application of geospatial techniques. A case study of Kali River, Western Uttar Pradesh, India, is selected for the analysis. The monitoring was carried out at 16 sampling locations. The discrete and diffuse pollution loads at different sampling sites were estimated and accounted using modified Sanders approach, whereas the monitored physical and chemical water quality parameters were utilized as inputs for FA/PCA. The designed optimum number of sampling locations for monsoon and non-monsoon seasons by modified Sanders approach are eight and seven while that for FA/PCA are eleven and nine, respectively. Less variation in the number and locations of designed sampling sites were obtained by both techniques, which shows stability of results. A geospatial analysis has also been carried out to check the significance of designed sampling location with respect to river basin characteristics and land use of the study area. Both methods are equally efficient; however, modified Sanders

  18. Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques--a case study

    International Nuclear Information System (INIS)

    Singh, Kunwar P.; Malik, Amrita; Sinha, Sarita

    2005-01-01

    Multivariate statistical techniques, such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the data set on water quality of the Gomti river (India), generated during three years (1999-2001) monitoring at eight different sites for 34 parameters (9792 observations). This study presents usefulness of multivariate statistical techniques for evaluation and interpretation of large complex water quality data sets and apportionment of pollution sources/factors with a view to get better information about the water quality and design of monitoring network for effective management of water resources. Three significant groups, upper catchments (UC), middle catchments (MC) and lower catchments (LC) of sampling sites were obtained through CA on the basis of similarity between them. FA/PCA applied to the data sets pertaining to three catchments regions of the river resulted in seven, seven and six latent factors, respectively responsible for the data structure, explaining 74.3, 73.6 and 81.4% of the total variance of the respective data sets. These included the trace metals group (leaching from soil and industrial waste disposal sites), organic pollution group (municipal and industrial effluents), nutrients group (agricultural runoff), alkalinity, hardness, EC and solids (soil leaching and runoff process). DA showed the best results for data reduction and pattern recognition during both temporal and spatial analysis. It rendered five parameters (temperature, total alkalinity, Cl, Na and K) affording more than 94% right assignations in temporal analysis, while 10 parameters (river discharge, pH, BOD, Cl, F, PO 4 , NH 4 -N, NO 3 -N, TKN and Zn) to afford 97% right assignations in spatial analysis of three different regions in the basin. Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. Further

  19. A PERFORMANCE COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORKS AND MULTIVARIATE STATISTICAL METHODS IN FORECASTING FINANCIAL STRENGTH RATING IN TURKISH BANKING SECTOR

    Directory of Open Access Journals (Sweden)

    MELEK ACAR BOYACIOĞLU

    2013-06-01

    Full Text Available Financial strength rating indicates the fundamental financial strength of a bank. The aim of financial strength rating is to measure a bank’s fundamental financial strength excluding the external factors. External factors can stem from the working environment or can be linked with the outside protective support mechanisms. With the evaluation, the rating of a bank free from outside supportive factors is being sought. Also the financial fundamental, franchise value, the variety of assets and working environment of a bank are being evaluated in this context. In this study, a model has been developed in order to predict the financial strength rating of Turkish banks. The methodology of this study is as follows: Selecting variables to be used in the model, creating a data set, choosing the techniques to be used and the evaluation of classification success of the techniques. It is concluded that the artificial neural network system shows a better performance in terms of classification of financial strength rating in comparison to multivariate statistical methods in the raining set. On the other hand, there is no meaningful difference could be found in the validation set in which the prediction performances of the employed techniques are tested.

  20. [Retrospective statistical analysis of clinical factors of recurrence in chronic subdural hematoma: correlation between univariate and multivariate analysis].

    Science.gov (United States)

    Takayama, Motoharu; Terui, Keita; Oiwa, Yoshitsugu

    2012-10-01

    Chronic subdural hematoma is common in elderly individuals and surgical procedures are simple. The recurrence rate of chronic subdural hematoma, however, varies from 9.2 to 26.5% after surgery. The authors studied factors of the recurrence using univariate and multivariate analyses in patients with chronic subdural hematoma We retrospectively reviewed 239 consecutive cases of chronic subdural hematoma who received burr-hole surgery with irrigation and closed-system drainage. We analyzed the relationships between recurrence of chronic subdural hematoma and factors such as sex, age, laterality, bleeding tendency, other complicated diseases, density on CT, volume of the hematoma, residual air in the hematoma cavity, use of artificial cerebrospinal fluid. Twenty-one patients (8.8%) experienced a recurrence of chronic subdural hematoma. Multiple logistic regression found that the recurrence rate was higher in patients with a large volume of the residual air, and was lower in patients using artificial cerebrospinal fluid. No statistical differences were found in bleeding tendency. Techniques to reduce the air in the hematoma cavity are important for good outcome in surgery of chronic subdural hematoma. Also, the use of artificial cerebrospinal fluid reduces recurrence of chronic subdural hematoma. The surgical procedures can be the same for patients with bleeding tendencies.

  1. Integrated Application of Multivariate Statistical Methods to Source Apportionment of Watercourses in the Liao River Basin, Northeast China

    Directory of Open Access Journals (Sweden)

    Jiabo Chen

    2016-10-01

    Full Text Available Source apportionment of river water pollution is critical in water resource management and aquatic conservation. Comprehensive application of various GIS-based multivariate statistical methods was performed to analyze datasets (2009–2011 on water quality in the Liao River system (China. Cluster analysis (CA classified the 12 months of the year into three groups (May–October, February–April and November–January and the 66 sampling sites into three groups (groups A, B and C based on similarities in water quality characteristics. Discriminant analysis (DA determined that temperature, dissolved oxygen (DO, pH, chemical oxygen demand (CODMn, 5-day biochemical oxygen demand (BOD5, NH4+–N, total phosphorus (TP and volatile phenols were significant variables affecting temporal variations, with 81.2% correct assignments. Principal component analysis (PCA and positive matrix factorization (PMF identified eight potential pollution factors for each part of the data structure, explaining more than 61% of the total variance. Oxygen-consuming organics from cropland and woodland runoff were the main latent pollution factor for group A. For group B, the main pollutants were oxygen-consuming organics, oil, nutrients and fecal matter. For group C, the evaluated pollutants primarily included oxygen-consuming organics, oil and toxic organics.

  2. Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

    Science.gov (United States)

    Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

    2014-10-15

    Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. Spatial and temporal variation of water quality of a segment of Marikina River using multivariate statistical methods.

    Science.gov (United States)

    Chounlamany, Vanseng; Tanchuling, Maria Antonia; Inoue, Takanobu

    2017-09-01

    Payatas landfill in Quezon City, Philippines, releases leachate to the Marikina River through a creek. Multivariate statistical techniques were applied to study temporal and spatial variations in water quality of a segment of the Marikina River. The data set included 12 physico-chemical parameters for five monitoring stations over a year. Cluster analysis grouped the monitoring stations into four clusters and identified January-May as dry season and June-September as wet season. Principal components analysis showed that three latent factors are responsible for the data set explaining 83% of its total variance. The chemical oxygen demand, biochemical oxygen demand, total dissolved solids, Cl - and PO 4 3- are influenced by anthropogenic impact/eutrophication pollution from point sources. Total suspended solids, turbidity and SO 4 2- are influenced by rain and soil erosion. The highest state of pollution is at the Payatas creek outfall from March to May, whereas at downstream stations it is in May. The current study indicates that the river monitoring requires only four stations, nine water quality parameters and testing over three specific months of the year. The findings of this study imply that Payatas landfill requires a proper leachate collection and treatment system to reduce its impact on the Marikina River.

  4. Multivariate statistical analysis to characterize/discriminate between anthropogenic and geogenic trace elements occurrence in the Campania Plain, Southern Italy.

    Science.gov (United States)

    Busico, Gianluigi; Cuoco, Emilio; Kazakis, Nerantzis; Colombani, Nicolò; Mastrocicco, Micòl; Tedesco, Dario; Voudouris, Konstantinos

    2018-03-01

    Shallow aquifers are the most accessible reservoirs of potable groundwater; nevertheless, they are also prone to various sources of pollution and it is usually difficult to distinguish between human and natural sources at the watershed scale. The area chosen for this study (the Campania Plain) is characterized by high spatial heterogeneities both in geochemical features and in hydraulic properties. Groundwater mineralization is driven by many processes such as, geothermal activity, weathering of volcanic products and intense human activities. In such a landscape, multivariate statistical analysis has been used to differentiate among the main hydrochemical processes occurring in the area, using three different approaches of factor analysis: (i) major elements, (ii) trace elements, (iii) both major and trace elements. The elaboration of the factor analysis approaches has revealed seven distinct hydrogeochemical processes: i) Salinization (Cl - , Na + ); ii) Carbonate rocks dissolution; iii) Anthropogenic inputs (NO 3 - , SO 4 2- , U, V); iv) Reducing conditions (Fe 2+ , Mn 2+ ); v) Heavy metals contamination (Cr and Ni); vi) Geothermal fluids influence (Li + ); and vii) Volcanic products contribution (As, Rb). Results from this study highlight the need to separately apply factor analysis when a large data set of trace elements is available. In fact, the impact of geothermal fluids in the shallow aquifer was identified from the application of the factor analysis using only trace elements. This study also reveals that the factor analysis of major and trace elements can differentiate between anthropogenic and geogenic sources of pollution in intensively exploited aquifers. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Hydrochemical Characteristics and Multivariate Statistical Analysis of Natural Water System: A Case Study in Kangding County, Southwestern China

    Directory of Open Access Journals (Sweden)

    Yunhui Zhang

    2018-01-01

    Full Text Available The utilization for water resource has been of great concern to human life. To assess the natural water system in Kangding County, the integrated methods of hydrochemical analysis, multivariate statistics and geochemical modelling were conducted on surface water, groundwater, and thermal water samples. Surface water and groundwater were dominated by Ca-HCO3 type, while thermal water belonged to Ca-HCO3 and Na-Cl-SO4 types. The analyzing results concluded the driving factors that affect hydrochemical components. Following the results of the combined assessments, hydrochemical process was controlled by the dissolution of carbonate and silicate minerals with slight influence from anthropogenic activity. The mixing model of groundwater and thermal water was calculated using silica-enthalpy method, yielding cold-water fraction of 0.56–0.79 and an estimated reservoir temperature of 130–199 °C, respectively. δD and δ18O isotopes suggested that surface water, groundwater and thermal springs were of meteoric origin. Thermal water should have deep circulation through the Xianshuihe fault zone, while groundwater flows through secondary fractures where it recharges with thermal water. Those analytical results were used to construct a hydrological conceptual model, providing a better understanding of the natural water system in Kangding County.

  6. Ultimate compression after impact load prediction in graphite/epoxy coupons using neural network and multivariate statistical analyses

    Science.gov (United States)

    Gregoire, Alexandre David

    2011-07-01

    The goal of this research was to accurately predict the ultimate compressive load of impact damaged graphite/epoxy coupons using a Kohonen self-organizing map (SOM) neural network and multivariate statistical regression analysis (MSRA). An optimized use of these data treatment tools allowed the generation of a simple, physically understandable equation that predicts the ultimate failure load of an impacted damaged coupon based uniquely on the acoustic emissions it emits at low proof loads. Acoustic emission (AE) data were collected using two 150 kHz resonant transducers which detected and recorded the AE activity given off during compression to failure of thirty-four impacted 24-ply bidirectional woven cloth laminate graphite/epoxy coupons. The AE quantification parameters duration, energy and amplitude for each AE hit were input to the Kohonen self-organizing map (SOM) neural network to accurately classify the material failure mechanisms present in the low proof load data. The number of failure mechanisms from the first 30% of the loading for twenty-four coupons were used to generate a linear prediction equation which yielded a worst case ultimate load prediction error of 16.17%, just outside of the +/-15% B-basis allowables, which was the goal for this research. Particular emphasis was placed upon the noise removal process which was largely responsible for the accuracy of the results.

  7. Discrimination between glycosylation patterns of therapeutic antibodies using a microfluidic platform, MALDI-MS and multivariate statistics.

    Science.gov (United States)

    Thuy, Tran Thi; Tengstrand, Erik; Aberg, Magnus; Thorsén, Gunnar

    2012-11-01

    Optimal glycosylation with respect to the efficacy, serum half-life time, and immunogenic properties is essential in the generation of therapeutic antibodies. The glycosylation pattern can be affected by several different parameters during the manufacture of antibodies and may change significantly over cultivation time. Fast and robust methods for determination of the glycosylation patterns of therapeutic antibodies are therefore needed. We have recently presented an efficient method for the determination of glycans on therapeutic antibodies using a microfluidic CD platform for sample preparation prior to matrix-assisted laser-desorption mass spectrometry analysis. In the present work, this method is applied to analyse the glycosylation patterns of three commercially available therapeutic antibodies and one intended for therapeutic use. Two of the antibodies produced in mouse myeloma cell line (SP2/0) and one produced in Chinese hamster ovary (CHO) cells exhibited similar glycosylation patterns but could still be readily differentiated from each other using multivariate statistical methods. The two antibodies with most similar glycosylation patterns were also studied in an assessment of the method's applicability for quality control of therapeutic antibodies. The method presented in this paper is highly automated and rapid. It can therefore efficiently generate data that helps to keep a production process within the desired design space or assess that an identical product is being produced after changes to the process. Copyright © 2012 Elsevier B.V. All rights reserved.

  8. Fragments analysis of Marajoara pubic covers using a portable system of X-ray fluorescence and multivariate statistics

    International Nuclear Information System (INIS)

    Freitas, Renato; Rabello, Angela; Lima, Tania

    2011-01-01

    Full text: In this work it was characterized the elemental composition of 102 fragments of Marajoara pubic covers, belonging to the National Museum collection, using EDXRF and multivariate statistics analysis. The objective was to identify possible groups of samples that presented similar characteristics. This information will be useful in the development of a systematic classification of these artifacts. Provenance studies of ancient ceramics are based on the assumption that pottery produced from a specific clay will present a similar chemical composition, which will distinguish them from pottery produced from a different clay. In this way, the pottery is assigned to particular production groups, which are then correlated with their respective origins. EDXRF measurements were carried out with a portable system, developed in the Nuclear Instrumentation Laboratory, consisting of an X-ray tube Oxford TF3005 with tungsten (W) anode, operating at 25 kV and 100 μA, and a Si-PIN XR-100CR detector from Amptek. In each one of the 102 fragments, six points were analyzed (three in the front part and three in the reverse) with an acquisition time of 600 s and a beam collimation of 2 mm. The spectra were processed and analyzed using the software QXAS-AXIL from IAEA. PCA was applied to the XRF results revealing a clear cluster separation to the samples. (author)

  9. Extracting climate signals from large hydrological data cubes using multivariate statistics - an example for the Mediterranean basin

    Science.gov (United States)

    Kauer, Agnes; Dorigo, Wouter; Bauer-Marschallinger, Bernhard

    2017-04-01

    Global warming is expected to change ocean-atmosphere oscillation patterns, e.g. the El Nino Southern Oscillation, and may thus have a substantial impact on water resources over land. Yet, the link between climate oscillations and terrestrial hydrology has large uncertainties. In particular, the climate in the Mediterranean basin is expected to be sensitive to global warming as it may increase insufficient and irregular water supply and lead to more frequent and intense droughts and heavy precipitation events. The ever increasing need for water in tourism and agriculture reinforce the problem. Therefore, the monitoring and better understanding of the hydrological cycle are crucial for this area. This study seeks to quantify the effect of regional climate modes, e.g. the Northern Atlantic Oscillation (NAO) on the hydrological cycle in the Mediterranean. We apply Empirical Orthogonal Functions (EOF) to a wide range of hydrological datasets to extract the major modes of variation over the study period. We use more than ten datasets describing precipitation, soil moisture, evapotranspiration, and changes in water mass with study periods ranging from one to three decades depending on the dataset. The resulting EOFs are then examined for correlations with regional climate modes using Spearman rank correlation analysis. This is done for the entire time span of the EOFs and for monthly and seasonally sampled data. We find relationships between the hydrological datasets and the climate modes NAO, Arctic Oscillation (AO), Eastern Atlantic (EA), and Tropical Northern Atlantic (TNA). Analyses of monthly and seasonally sampled data reveal high correlations especially in the winter months. However, the spatial extent of the data cube considered for the analyses have a large impact on the results. Our statistical analyses suggest an impact of regional climate modes on the hydrological cycle in the Mediterranean area and may provide valuable input for evaluating process

  10. Multi-Site and Multi-Variables Statistical Downscaling Technique in the Monsoon Dominated Region of Pakistan

    Science.gov (United States)

    Khan, Firdos; Pilz, Jürgen

    2016-04-01

    South Asia is under the severe impacts of changing climate and global warming. The last two decades showed that climate change or global warming is happening and the first decade of 21st century is considered as the warmest decade over Pakistan ever in history where temperature reached 53 0C in 2010. Consequently, the spatio-temporal distribution and intensity of precipitation is badly effected and causes floods, cyclones and hurricanes in the region which further have impacts on agriculture, water, health etc. To cope with the situation, it is important to conduct impact assessment studies and take adaptation and mitigation remedies. For impact assessment studies, we need climate variables at higher resolution. Downscaling techniques are used to produce climate variables at higher resolution; these techniques are broadly divided into two types, statistical downscaling and dynamical downscaling. The target location of this study is the monsoon dominated region of Pakistan. One reason for choosing this area is because the contribution of monsoon rains in this area is more than 80 % of the total rainfall. This study evaluates a statistical downscaling technique which can be then used for downscaling climatic variables. Two statistical techniques i.e. quantile regression and copula modeling are combined in order to produce realistic results for climate variables in the area under-study. To reduce the dimension of input data and deal with multicollinearity problems, empirical orthogonal functions will be used. Advantages of this new method are: (1) it is more robust to outliers as compared to ordinary least squares estimates and other estimation methods based on central tendency and dispersion measures; (2) it preserves the dependence among variables and among sites and (3) it can be used to combine different types of distributions. This is important in our case because we are dealing with climatic variables having different distributions over different meteorological

  11. metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

    Science.gov (United States)

    Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

    2016-07-01

    A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  12. Multivariate statistical analysis of the hydrogeochemical and isotopic composition of the groundwater resources in northeastern Peloponnesus (Greece).

    Science.gov (United States)

    Matiatos, Ioannis; Alexopoulos, Apostolos; Godelitsas, Athanasios

    2014-04-01

    The present study involves an integration of the hydrogeological, hydrochemical and isotopic (both stable and radiogenic) data of the groundwater samples taken from aquifers occurring in the region of northeastern Peloponnesus. Special emphasis has been given to health-related ions and isotopes in relation to the WHO and USEPA guidelines, to highlight the concentrations of compounds (e.g., As and Ba) exceeding the drinking water thresholds. Multivariate statistical analyses, i.e. two principal component analyses (PCA) and one discriminant analysis (DA), combined with conventional hydrochemical methodologies, were applied, with the aim to interpret the spatial variations in the groundwater quality and to identify the main hydrogeochemical factors and human activities responsible for the high ion concentrations and isotopic content in the groundwater analysed. The first PCA resulted in a three component model, which explained approximately 82% of the total variance of the data sets and enabled the identification of the hydrogeological processes responsible for the isotopic content i.e., δ(18)Ο, tritium and (222)Rn. The second PCA, involving the trace element presence in the water samples, revealed a four component model, which explained approximately 89% of the total variance of the data sets, giving more insight into the geochemical and anthropogenic controls on the groundwater composition (e.g., water-rock interaction, hydrothermal activity and agricultural activities). Using discriminant analysis, a four parameter (δ(18)O, (Ca+Mg)/(HCO3+SO4), EC and Cl) discriminant function concerning the (222)Rn content was derived, which favoured a classification of the samples according to the concentration of (222)Rn as (222)Rn-safe (11 Bq·L(-1)). The selection of radon builds on the fact that this radiogenic isotope has been generally related to increased health risk when consumed. Copyright © 2014 Elsevier B.V. All rights reserved.

  13. Heavy metals in soils and sediments from Dongting Lake in China: occurrence, sources, and spatial distribution by multivariate statistical analysis.

    Science.gov (United States)

    Zhang, Yaxin; Tian, Ye; Shen, Maocai; Zeng, Guangming

    2018-03-03

    Heavy metal contamination in soils/sediments and its impact on human health and ecological environment have aroused wide concerns. Our study investigated 30 samples of soils and sediments around Dongting Lake to analyze the concentration of As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn in the samples and to distinguish the natural and anthropogenic sources. Also, the relationship between heavy metals and the physicochemical properties of samples was studied by multivariate statistical analysis. Concentration of Cd at most sampling sites were more than five times that of national environmental quality standard for soil in China (GB 15618-1995), and Pb and Zn levels exceeded one to two times. Moreover, Cr in the soil was higher than the national environmental quality standards for one to two times while in sediment was lower than the national standard. The investigation revealed that the accumulations of As, Cd, Mn, and Pb in the soils, and sediments were affected apparently by anthropogenic activities; however, Cr, Fe, and Ni levels were impacted by parent materials. Human activities around Dongting Lake mainly consisted of industrial activities, mining and smelting, sewage discharges, fossil fuel combustion, and agricultural chemicals. The spatial distribution of heavy metal in soil followed the rule of geographical gradient, whereas in sediments, it was significantly affected by the river basins and human activities. The result of principal component analysis (PCA) demonstrated that heavy metals in soils were associated with pH and total phosphorus (TP), while in sediments, As, Cr, Fe, and Ni were closely associated with cation exchange capacity (CEC) and pH, where Pb, Zn, and Cd were associated with total nitrogen (TN), TP, total carbon (TC), moisture content (MC), soil organic matter (SOM), and ignition lost (IL). Our research provides comprehensive approaches to better understand the potential sources and the fate of contaminants in lakeshore soils and sediments.

  14. Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods

    Directory of Open Access Journals (Sweden)

    Weili Duan

    2016-01-01

    Full Text Available Multivariate statistical methods including cluster analysis (CA, discriminant analysis (DA and component analysis/factor analysis (PCA/FA, were applied to explore the surface water quality datasets including 14 parameters at 28 sites of the Eastern Poyang Lake Basin, Jiangxi Province of China, from January 2012 to April 2015, characterize spatiotemporal variation in pollution and identify potential pollution sources. The 28 sampling stations were divided into two periods (wet season and dry season and two regions (low pollution and high pollution, respectively, using hierarchical CA method. Four parameters (temperature, pH, ammonia-nitrogen (NH4-N, and total nitrogen (TN were identified using DA to distinguish temporal groups with close to 97.86% correct assignations. Again using DA, five parameters (pH, chemical oxygen demand (COD, TN, Fluoride (F, and Sulphide (S led to 93.75% correct assignations for distinguishing spatial groups. Five potential pollution sources including nutrients pollution, oxygen consuming organic pollution, fluorine chemical pollution, heavy metals pollution and natural pollution, were identified using PCA/FA techniques for both the low pollution region and the high pollution region. Heavy metals (Cuprum (Cu, chromium (Cr and Zinc (Zn, fluoride and sulfide are of particular concern in the study region because of many open-pit copper mines such as Dexing Copper Mine. Results obtained from this study offer a reasonable classification scheme for low-cost monitoring networks. The results also inform understanding of spatio-temporal variation in water quality as these topics relate to water resources management.

  15. Water quality assessment in the Bétaré-Oya gold mining area (East-Cameroon): Multivariate Statistical Analysis approach.

    Science.gov (United States)

    Rakotondrabe, Felaniaina; Ndam Ngoupayou, Jules Remy; Mfonka, Zakari; Rasolomanana, Eddy Harilala; Nyangono Abolo, Alexis Jacob; Ako Ako, Andrew

    2018-01-01

    The influence of gold mining activities on the water quality in the Mari catchment in Bétaré-Oya (East Cameroon) was assessed in this study. Sampling was performed within the period of one hydrological year (2015 to 2016), with 22 sampling sites consisting of groundwater (06) and surface water (16). In addition to measuring the physicochemical parameters, such as pH, electrical conductivity, alkalinity, turbidity, suspended solids and CN - , eleven major elements (Na + , K + , Ca 2+ , Mg 2+ , NH 4 + , Cl - , NO 3 - , HCO 3 - , SO 4 2- , PO 4 3- and F - ) and eight heavy metals (Pb, Zn, Cd, Fe, Cu, As, Mn and Cr) were also analyzed using conventional hydrochemical methods, Multivariate Statistical Analysis and the Heavy metal Pollution Index (HPI). The results showed that the water from Mari catchment and Lom River was acidic to basic (5.40water quality, except for nitrates in some wells, which was found at a concentration >50mg NO 3 - /L. This water was found as two main types: calcium magnesium bicarbonate (CaMg-HCO 3 ), which was the most represented, and sodium bicarbonate potassium (NaK-HCO 3 ). As for trace elements in surface water, the contents of Pb, Cd, Mn, Cr and Fe were higher than recommended by the WHO guidelines, and therefore, the surface water was unsuitable for human consumption. Three phenomena were responsible for controlling the quality of the water in the study area: hydrolysis of silicate minerals of plutono-metamorphic rocks, which constitute the geological basement of this area; vegetation and soil leaching; and mining activities. The high concentrations of TSS and trace elements found in this basin were mainly due to gold mining activities (exploration and exploitation) as well as digging of rivers beds, excavation and gold amalgamation. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. The assessment of processes controlling the spatial distribution of hydrogeochemical groundwater types in Mali using multivariate statistics

    Science.gov (United States)

    Keita, Souleymane; Zhonghua, Tang

    2017-10-01

    Sustainable management of groundwater resources is a major issue for developing countries, especially in Mali. The multiple uses of groundwater led countries to promote sound management policies for sustainable use of the groundwater resources. For this reason, each country needs data enabling it to monitor and predict the changes of the resources. Also given the importance of groundwater quality changes often marked by the recurrence of droughts; the potential impacts of regional and geological setting of groundwater resources requires careful study. Unfortunately, recent decades have seen a considerable reduction of national capacities to ensure the hydrogeological monitoring and production of qualit data for decision making. The purpose of this work is to use the groundwater data and translate into useful information that can improve water resources management capacity in Mali. In this paper, we used groundwater analytical data from accredited, laboratories in Mali to carry out a national scale assessment of the groundwater types and their distribution. We, adapted multivariate statistical methods to classify 2035 groundwater samples into seven main groundwater types and built a national scale map from the results. We used a two-level K-mean clustering technique to examine the hydro-geochemical records as percentages of the total concentrations of major ions, namely sodium (Na), magnesium (Mg), calcium (Ca), chloride (Cl), bicarbonate (HCO3), and sulphate (SO4). The first step of clustering formed 20 groups, and these groups were then re-clustered to produce the final seven groundwater types. The results were verified and confirmed using Principal Component Analysis (PCA) and RockWare (Aq.QA) software. We found that HCO3 was the most dominant anion throughout the country and that Cl and SO4 were only important in some local zones. The dominant cations were Na and Mg. Also, major ion ratios changed with geographical location and geological, and climatic

  17. Using Statistical Multivariable Models to Understand the Relationship Between Interplanetary Coronal Mass Ejecta and Magnetic Flux Ropes

    Science.gov (United States)

    Riley, P.; Richardson, I. G.

    2012-01-01

    In-situ measurements of interplanetary coronal mass ejections (ICMEs) display a wide range of properties. A distinct subset, "magnetic clouds" (MCs), are readily identifiable by a smooth rotation in an enhanced magnetic field, together with an unusually low solar wind proton temperature. In this study, we analyze Ulysses spacecraft measurements to systematically investigate five possible explanations for why some ICMEs are observed to be MCs and others are not: i) An observational selection effect; that is, all ICMEs do in fact contain MCs, but the trajectory of the spacecraft through the ICME determines whether the MC is actually encountered; ii) interactions of an erupting flux rope (PR) with itself or between neighboring FRs, which produce complex structures in which the coherent magnetic structure has been destroyed; iii) an evolutionary process, such as relaxation to a low plasma-beta state that leads to the formation of an MC; iv) the existence of two (or more) intrinsic initiation mechanisms, some of which produce MCs and some that do not; or v) MCs are just an easily identifiable limit in an otherwise corntinuous spectrum of structures. We apply quantitative statistical models to assess these ideas. In particular, we use the Akaike information criterion (AIC) to rank the candidate models and a Gaussian mixture model (GMM) to uncover any intrinsic clustering of the data. Using a logistic regression, we find that plasma-beta, CME width, and the ratio O(sup 7) / O(sup 6) are the most significant predictor variables for the presence of an MC. Moreover, the propensity for an event to be identified as an MC decreases with heliocentric distance. These results tend to refute ideas ii) and iii). GMM clustering analysis further identifies three distinct groups of ICMEs; two of which match (at the 86% level) with events independently identified as MCs, and a third that matches with non-MCs (68 % overlap), Thus, idea v) is not supported. Choosing between ideas i) and

  18. Total coliforms, arsenic and cadmium exposure through drinking water in the Western Region of Ghana: application of multivariate statistical technique to groundwater quality.

    Science.gov (United States)

    Affum, Andrews Obeng; Osae, Shiloh Dede; Nyarko, Benjamin Jabez Botwe; Afful, Samuel; Fianko, Joseph Richmond; Akiti, Tetteh Thomas; Adomako, Dickson; Acquaah, Samuel Osafo; Dorleku, Micheal; Antoh, Emmanuel; Barnes, Felix; Affum, Enoch Acheampong

    2015-02-01

    In recent times, surface water resource in the Western Region of Ghana has been found to be inadequate in supply and polluted by various anthropogenic activities. As a result of these problems, the demand for groundwater by the human populations in the peri-urban communities for domestic, municipal and irrigation purposes has increased without prior knowledge of its water quality. Water samples were collected from 14 public hand-dug wells during the rainy season in 2013 and investigated for total coliforms, Escherichia coli, mercury (Hg), arsenic (As), cadmium (Cd) and physicochemical parameters. Multivariate statistical analysis of the dataset and a linear stoichiometric plot of major ions were applied to group the water samples and to identify the main factors and sources of contamination. Hierarchal cluster analysis revealed four clusters from the hydrochemical variables (R-mode) and three clusters in the case of water samples (Q-mode) after z score standardization. Principal component analysis after a varimax rotation of the dataset indicated that the four factors extracted explained 93.3 % of the total variance, which highlighted salinity, toxic elements and hardness pollution as the dominant factors affecting groundwater quality. Cation exchange, mineral dissolution and silicate weathering influenced groundwater quality. The ranking order of major ions was Na(+) > Ca(2+) > K(+) > Mg(2+) and Cl(-) > SO4 (2-) > HCO3 (-). Based on piper plot and the hydrogeology of the study area, sodium chloride (86 %), sodium hydrogen carbonate and sodium carbonate (14 %) water types were identified. Although E. coli were absent in the water samples, 36 % of the wells contained total coliforms (Enterobacter species) which exceeded the WHO guidelines limit of zero colony-forming unit (CFU)/100 mL of drinking water. With the exception of Hg, the concentration of As and Cd in 79 and 43 % of the water samples exceeded the WHO guideline limits of 10 and 3

  19. Metal contamination in campus dust of Xi'an, China: A study based on multivariate statistics and spatial distribution

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Hao [School of Tourism and Environment, Shaanxi Normal University, Xi' an 710062 (China); Lu, Xinwei, E-mail: luxinwei@snnu.edu.cn [School of Tourism and Environment, Shaanxi Normal University, Xi' an 710062 (China); Li, Loretta Y., E-mail: lli@civil.ubc.ca [Department of Civil Engineering, University of British Columbia, Vancouver V6T 1Z4 (Canada); Gao, Tianning; Chang, Yuyu [School of Tourism and Environment, Shaanxi Normal University, Xi' an 710062 (China)

    2014-06-01

    The concentrations of As, Ba, Co, Cr, Cu, Mn, Ni, Pb, V and Zn in campus dust from kindergartens, elementary schools, middle schools and universities of Xi'an, China were determined by X-ray fluorescence spectrometry. Correlation coefficient analysis, principal component analysis (PCA) and cluster analysis (CA) were used to analyze the data and to identify possible sources of these metals in the dust. The spatial distributions of metals in urban dust of Xi'an were analyzed based on the metal concentrations in campus dusts using the geostatistics method. The results indicate that dust samples from campuses have elevated metal concentrations, especially for Pb, Zn, Co, Cu, Cr and Ba, with the mean values of 7.1, 5.6, 3.7, 2.9, 2.5 and 1.9 times the background values for Shaanxi soil, respectively. The enrichment factor results indicate that Mn, Ni, V, As and Ba in the campus dust were deficiently to minimally enriched, mainly affected by nature and partly by anthropogenic sources, while Co, Cr, Cu, Pb and Zn in the campus dust and especially Pb and Zn were mostly affected by human activities. As and Cu, Mn and Ni, Ba and V, and Pb and Zn had similar distribution patterns. The southwest high-tech industrial area and south commercial and residential areas have relatively high levels of most metals. Three main sources were identified based on correlation coefficient analysis, PCA, CA, as well as spatial distribution characteristics. As, Ni, Cu, Mn, Pb, Zn and Cr have mixed sources — nature, traffic, as well as fossil fuel combustion and weathering of materials. Ba and V are mainly derived from nature, but partly also from industrial emissions, as well as construction sources, while Co principally originates from construction. - Highlights: • Metal content in dust from schools was determined by XRF. • Spatial distribution of metals in urban dust was focused on campus samples. • Multivariate statistic and spatial distribution were used to identify metal

  20. Metal contamination in campus dust of Xi'an, China: A study based on multivariate statistics and spatial distribution

    International Nuclear Information System (INIS)

    Chen, Hao; Lu, Xinwei; Li, Loretta Y.; Gao, Tianning; Chang, Yuyu

    2014-01-01

    The concentrations of As, Ba, Co, Cr, Cu, Mn, Ni, Pb, V and Zn in campus dust from kindergartens, elementary schools, middle schools and universities of Xi'an, China were determined by X-ray fluorescence spectrometry. Correlation coefficient analysis, principal component analysis (PCA) and cluster analysis (CA) were used to analyze the data and to identify possible sources of these metals in the dust. The spatial distributions of metals in urban dust of Xi'an were analyzed based on the metal concentrations in campus dusts using the geostatistics method. The results indicate that dust samples from campuses have elevated metal concentrations, especially for Pb, Zn, Co, Cu, Cr and Ba, with the mean values of 7.1, 5.6, 3.7, 2.9, 2.5 and 1.9 times the background values for Shaanxi soil, respectively. The enrichment factor results indicate that Mn, Ni, V, As and Ba in the campus dust were deficiently to minimally enriched, mainly affected by nature and partly by anthropogenic sources, while Co, Cr, Cu, Pb and Zn in the campus dust and especially Pb and Zn were mostly affected by human activities. As and Cu, Mn and Ni, Ba and V, and Pb and Zn had similar distribution patterns. The southwest high-tech industrial area and south commercial and residential areas have relatively high levels of most metals. Three main sources were identified based on correlation coefficient analysis, PCA, CA, as well as spatial distribution characteristics. As, Ni, Cu, Mn, Pb, Zn and Cr have mixed sources — nature, traffic, as well as fossil fuel combustion and weathering of materials. Ba and V are mainly derived from nature, but partly also from industrial emissions, as well as construction sources, while Co principally originates from construction. - Highlights: • Metal content in dust from schools was determined by XRF. • Spatial distribution of metals in urban dust was focused on campus samples. • Multivariate statistic and spatial distribution were used to identify metal sources.

  1. ArraySolver: an algorithm for colour-coded graphical display and Wilcoxon signed-rank statistics for comparing microarray gene expression data.

    Science.gov (United States)

    Khan, Haseeb Ahmad

    2004-01-01

    The massive surge in the production of microarray data poses a great challenge for proper analysis and interpretation. In recent years numerous computational tools have been developed to extract meaningful interpretation of microarray gene expression data. However, a convenient tool for two-groups comparison of microarray data is still lacking and users have to rely on commercial statistical packages that might be costly and require special skills, in addition to extra time and effort for transferring data from one platform to other. Various statistical methods, including the t-test, analysis of variance, Pearson test and Mann-Whitney U test, have been reported for comparing microarray data, whereas the utilization of the Wilcoxon signed-rank test, which is an appropriate test for two-groups comparison of gene expression data, has largely been neglected in microarray studies. The aim of this investigation was to build an integrated tool, ArraySolver, for colour-coded graphical display and comparison of gene expression data using the Wilcoxon signed-rank test. The results of software validation showed similar outputs with ArraySolver and SPSS for large datasets. Whereas the former program appeared to be more accurate for 25 or fewer pairs (n < or = 25), suggesting its potential application in analysing molecular signatures that usually contain small numbers of genes. The main advantages of ArraySolver are easy data selection, convenient report format, accurate statistics and the familiar Excel platform.

  2. Understanding the groundwater dynamics in the Southern Rift Valley Lakes Basin (Ethiopia). Multivariate statistical analysis method, oxygen (δ 18O) and deuterium (δ 2H)

    International Nuclear Information System (INIS)

    Girum Admasu Nadew; Zebene Lakew Tefera

    2013-01-01

    Multivariate statistical analysis is very important to classify waters of different hydrochemical groups. Statistical techniques, such as cluster analysis, can provide a powerful tool for analyzing water chemistry data. This method is used to test water quality data and determine if samples can be grouped into distinct populations that may be significant in the geologic context, as well as from a statistical point of view. Multivariate statistical analysis method is applied to the geochemical data in combination with δ 18 O and δ 2 H isotopes with the objective to understand the dynamics of groundwater using hierarchical clustering and isotope analyses. The geochemical and isotope data of the central and southern rift valley lakes have been collected and analyzed from different works. Isotope analysis shows that most springs and boreholes are recharged by July and August rainfalls. The different hydrochemical groups that resulted from the multivariate analysis are described and correlated with the geology of the area and whether it has any interaction with a system or not. (author)

  3. Detecting relationships between the interannual variability in climate records and ecological time series using a multivariate statistical approach - four case studies for the North Sea region

    Energy Technology Data Exchange (ETDEWEB)

    Heyen, H. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Gewaesserphysik

    1998-12-31

    A multivariate statistical approach is presented that allows a systematic search for relationships between the interannual variability in climate records and ecological time series. Statistical models are built between climatological predictor fields and the variables of interest. Relationships are sought on different temporal scales and for different seasons and time lags. The possibilities and limitations of this approach are discussed in four case studies dealing with salinity in the German Bight, abundance of zooplankton at Helgoland Roads, macrofauna communities off Norderney and the arrival of migratory birds on Helgoland. (orig.) [Deutsch] Ein statistisches, multivariates Modell wird vorgestellt, das eine systematische Suche nach potentiellen Zusammenhaengen zwischen Variabilitaet in Klima- und oekologischen Zeitserien erlaubt. Anhand von vier Anwendungsbeispielen wird der Klimaeinfluss auf den Salzgehalt in der Deutschen Bucht, Zooplankton vor Helgoland, Makrofauna vor Norderney, und die Ankunft von Zugvoegeln auf Helgoland untersucht. (orig.)

  4. MODEL APPLICATION MULTIVARIATE ANALYSIS OF STATISTICAL TECHNIQUES PCA AND HCA ASSESSMENT QUESTIONNAIRE ON CUSTOMER SATISFACTION: CASE STUDY IN A METALLURGICAL COMPANY OF METAL CONTAINERS

    Directory of Open Access Journals (Sweden)

    Cláudio Roberto Rosário

    2012-07-01

    Full Text Available The purpose of this research is to improve the practice on customer satisfaction analysis The article presents an analysis model to analyze the answers of a customer satisfaction evaluation in a systematic way with the aid of multivariate statistical techniques, specifically, exploratory analysis with PCA – Partial Components Analysis with HCA - Hierarchical Cluster Analysis. It was tried to evaluate the applicability of the model to be used by the issue company as a tool to assist itself on identifying the value chain perceived by the customer when applied the questionnaire of customer satisfaction. It was found with the assistance of multivariate statistical analysis that it was observed similar behavior among customers. It also allowed the company to conduct reviews on questions of the questionnaires, using analysis of the degree of correlation between the questions that was not a company’s practice before this research.

  5. Human Exposure Risk Assessment Due to Heavy Metals in Groundwater by Pollution Index and Multivariate Statistical Methods: A Case Study from South Africa

    OpenAIRE

    Vetrimurugan Elumalai; K. Brindha; Elango Lakshmanan

    2017-01-01

    Heavy metals in surface and groundwater were analysed and their sources were identified using multivariate statistical tools for two towns in South Africa. Human exposure risk through the drinking water pathway was also assessed. Electrical conductivity values showed that groundwater is desirable to permissible for drinking except for six locations. Concentration of aluminium, lead and nickel were above the permissible limit for drinking at all locations. Boron, cadmium, iron and manganese ex...

  6. ArrayVigil: a methodology for statistical comparison of gene signatures using segregated-one-tailed (SOT) Wilcoxon's signed-rank test.

    Science.gov (United States)

    Khan, Haseeb Ahmad

    2005-01-28

    Due to versatile diagnostic and prognostic fidelity molecular signatures or fingerprints are anticipated as the most powerful tools for cancer management in the near future. Notwithstanding the experimental advancements in microarray technology, methods for analyzing either whole arrays or gene signatures have not been firmly established. Recently, an algorithm, ArraySolver has been reported by Khan for two-group comparison of microarray gene expression data using two-tailed Wilcoxon signed-rank test. Most of the molecular signatures are composed of two sets of genes (hybrid signatures) wherein up-regulation of one set and down-regulation of the other set collectively define the purpose of a gene signature. Since the direction of a selected gene's expression (positive or negative) with respect to a particular disease condition is known, application of one-tailed statistics could be a more relevant choice. A novel method, ArrayVigil, is described for comparing hybrid signatures using segregated-one-tailed (SOT) Wilcoxon signed-rank test and the results compared with integrated-two-tailed (ITT) procedures (SPSS and ArraySolver). ArrayVigil resulted in lower P values than those obtained from ITT statistics while comparing real data from four signatures.

  7. Noncentral Chi-Square versus Normal Distributions in Describing the Likelihood Ratio Statistic: The Univariate Case and Its Multivariate Implication

    Science.gov (United States)

    Yuan, Ke-Hai

    2008-01-01

    In the literature of mean and covariance structure analysis, noncentral chi-square distribution is commonly used to describe the behavior of the likelihood ratio (LR) statistic under alternative hypothesis. Due to the inaccessibility of the rather technical literature for the distribution of the LR statistic, it is widely believed that the…

  8. Structure formation from non-Gaussian initial conditions: Multivariate biasing, statistics, and comparison with N-body simulations

    International Nuclear Information System (INIS)

    Giannantonio, Tommaso; Porciani, Cristiano

    2010-01-01

    We study structure formation in the presence of primordial non-Gaussianity of the local type with parameters f NL and g NL . We show that the distribution of dark-matter halos is naturally described by a multivariate bias scheme where the halo overdensity depends not only on the underlying matter density fluctuation δ but also on the Gaussian part of the primordial gravitational potential φ. This corresponds to a non-local bias scheme in terms of δ only. We derive the coefficients of the bias expansion as a function of the halo mass by applying the peak-background split to common parametrizations for the halo mass function in the non-Gaussian scenario. We then compute the halo power spectrum and halo-matter cross spectrum in the framework of Eulerian perturbation theory up to third order. Comparing our results against N-body simulations, we find that our model accurately describes the numerical data for wave numbers k≤0.1-0.3h Mpc -1 depending on redshift and halo mass. In our multivariate approach, perturbations in the halo counts trace φ on large scales, and this explains why the halo and matter power spectra show different asymptotic trends for k→0. This strongly scale-dependent bias originates from terms at leading order in our expansion. This is different from what happens using the standard univariate local bias where the scale-dependent terms come from badly behaved higher-order corrections. On the other hand, our biasing scheme reduces to the usual local bias on smaller scales, where |φ| is typically much smaller than the density perturbations. We finally discuss the halo bispectrum in the context of multivariate biasing and show that, due to its strong scale and shape dependence, it is a powerful tool for the detection of primordial non-Gaussianity from future galaxy surveys.

  9. Discrimination of irradiated MOX fuel from UOX fuel by multivariate statistical analysis of simulated activities of gamma-emitting isotopes

    Science.gov (United States)

    Åberg Lindell, M.; Andersson, P.; Grape, S.; Hellesen, C.; Håkansson, A.; Thulin, M.

    2018-03-01

    This paper investigates how concentrations of certain fission products and their related gamma-ray emissions can be used to discriminate between uranium oxide (UOX) and mixed oxide (MOX) type fuel. Discrimination of irradiated MOX fuel from irradiated UOX fuel is important in nuclear facilities and for transport of nuclear fuel, for purposes of both criticality safety and nuclear safeguards. Although facility operators keep records on the identity and properties of each fuel, tools for nuclear safeguards inspectors that enable independent verification of the fuel are critical in the recovery of continuity of knowledge, should it be lost. A discrimination methodology for classification of UOX and MOX fuel, based on passive gamma-ray spectroscopy data and multivariate analysis methods, is presented. Nuclear fuels and their gamma-ray emissions were simulated in the Monte Carlo code Serpent, and the resulting data was used as input to train seven different multivariate classification techniques. The trained classifiers were subsequently implemented and evaluated with respect to their capabilities to correctly predict the classes of unknown fuel items. The best results concerning successful discrimination of UOX and MOX-fuel were acquired when using non-linear classification techniques, such as the k nearest neighbors method and the Gaussian kernel support vector machine. For fuel with cooling times up to 20 years, when it is considered that gamma-rays from the isotope 134Cs can still be efficiently measured, success rates of 100% were obtained. A sensitivity analysis indicated that these methods were also robust.

  10. Rank Dynamics

    Science.gov (United States)

    Gershenson, Carlos

    Studies of rank distributions have been popular for decades, especially since the work of Zipf. For example, if we rank words of a given language by use frequency (most used word in English is 'the', rank 1; second most common word is 'of', rank 2), the distribution can be approximated roughly with a power law. The same applies for cities (most populated city in a country ranks first), earthquakes, metabolism, the Internet, and dozens of other phenomena. We recently proposed ``rank diversity'' to measure how ranks change in time, using the Google Books Ngram dataset. Studying six languages between 1800 and 2009, we found that the rank diversity curves of languages are universal, adjusted with a sigmoid on log-normal scale. We are studying several other datasets (sports, economies, social systems, urban systems, earthquakes, artificial life). Rank diversity seems to be universal, independently of the shape of the rank distribution. I will present our work in progress towards a general description of the features of rank change in time, along with simple models which reproduce it

  11. Quality characterization and pollution source identification of surface water using multivariate statistical techniques, Nalagarh Valley, Himachal Pradesh, India

    Science.gov (United States)

    Herojeet, Rajkumar; Rishi, Madhuri S.; Lata, Renu; Dolma, Konchok

    2017-09-01

    Sirsa River flows through the central part of the Nalagarh valley, belongs to the rapid industrial belt of Baddi, Barotiwala and Nalagarh (BBN). The appraisal of surface water quality to ascertain its utility in such ecologically sensitive areas is need of the hour. The present study envisages the application of multivariate analysis, water utility class and conventional graphical representation to reveal the hidden factor responsible for deterioration of water quality and determine the hydrochemical facies and its evolution processes of water types in Nalagarh valley, India. The quality assessment is made by estimating pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness, major ions (Na+, K+, Ca2+, Mg2+, HCO3 -, Cl-, SO4 2-, NO3 - and PO4 3-), dissolved oxygen (DO), biological oxygen demand (BOD) and total coliform (TC) to determine its suitability for drinking and domestic purposes. The parameters like pH, TDS, TH, Ca2+, HCO3 -, Cl-, SO4 2-, NO3 - are within the desirable limit as per Bureau of Indian Standards (Indian Standard Drinking Water Specification (Second Edition) IS:10500. Indian Standard Institute, New Delhi, pp 1-18, 2012). Mg2+, Na+ and K+ ions for pre monsoon and EC during pre and post monsoon at few sites and approx 40% samples of BOD and TC for both seasons exceeds the permissible limits indicate organic contamination from human activities. Water quality classification for designated use indicates that maximum surface water samples are not suitable for drinking water source without conventional treatment. The result of piper trillinear and Chadha's diagram classified majority of surface water samples for both seasons fall in the fields of Ca2+-Mg2+-HCO3 - water type indicating temporary hardness. PCA and CA reveal that the surface water chemistry is influenced by natural factors such as weathering of minerals, ion exchange processes and anthropogenic factors. Thus, the present paper illustrates the importance of

  12. Mineralization model for Chahar Gonbad copper-gold deposit (Sirjan, using mineralogical, alteration and geochemical data and multivariate statistical methods

    Directory of Open Access Journals (Sweden)

    Seayed Jaber Yousefi

    2012-04-01

    Full Text Available The study area is located in southeastern Iran, about 110 km southwest of Kerman. Geologically, the area is composed of ophiolitic rocks, volcanic rocks, intrusive bodies and sedimentary rocks. Vein mineralization within andesite, andesitic basalt, andesitic tuffs occurred along the Chahar Gonbad fault. Sulfide mineralization in the ore deposit has taken place as dissemination, veins and veinlets in which pyrite and chalcopyrite are the most important ores. In this area, argillic, phyllic and propylitic alteration types are observed. Such elements as Au, Bi, Cu, S and Se are more enriched than others and the enrichment factors for these elements in comparison with background concentration are 321, 503, 393, 703 and 208, and with respect to Clark concentration are 401, 222, 532, 101 and 156, respectively. According to multivariate analysis, three major mineralization phases are recognized in the deposit. During the first phase, hydrothermal calcite veins are enriched in As, Cd, Pb, Zn and Ca, the second phase is manifested by the enrichment of sulfide veins in Cu, Au, Ag, Bi, Fe and S and the third phase mineralization includes Ni, Mn, Se and Sb as an intermediate level between the two previous phases.

  13. Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis.

    Science.gov (United States)

    Song, Seung Yeob; Lee, Young Koung; Kim, In-Jung

    2016-01-01

    A high-throughput screening system for Citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1100 cm(-1), 1300-1500 cm(-1), and 1500-1700 cm(-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five Citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from Citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R(2)) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to Citrus sugar and acid contents, excellent Citrus lines can be early detected with greater accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Sediment contaminants and biological effects in southern California: Use of a multivariate statistical approach to assess biological impact

    International Nuclear Information System (INIS)

    Maxon, C.L.; Barnett, A.M.; Diener, D.R.

    1997-01-01

    This study attempts to predict biological toxicity and benthic community impact in sediments collected from two southern California sites. Contaminant concentrations and grain size were evaluated as predictors using a two-step multivariate approach. The first step used principal component analysis (PCA) to describe contamination type and magnitude present at each site. Four dominant PC vectors, explaining 88% of the total variance, each corresponded to a unique physical and/or chemical signature. The four PC vectors, in decreasing order of importance, were: (1) high molecular weight polynuclear aromatic hydrocarbons (PAH), most likely from combusted or weathered petroleum; (2) low molecular weight alkylated PAH, primarily from weathered fuel product; (3) low molecular weight nonalkylated PAH, indicating a fresh petroleum-related origin; and (4) fine-grained sediments and metals. The second step used stepwise regression analysis to predict individual biological effects (dependent) variables using the four PC vectors as independent variables. Results showed that sediment grain size alone was the best predictor of amphipod mortality. Contaminant vectors showed discrete depositional areas independent of grain size. Neither contaminant concentrations nor PCA vectors were good predictors of biological effects, most likely due to the low concentrations in sediments

  15. Multivariate Statistics and Supervised Learning for Predictive Detection of Unintentional Islanding in Grid-Tied Solar PV Systems

    Directory of Open Access Journals (Sweden)

    Shashank Vyas

    2016-01-01

    Full Text Available Integration of solar photovoltaic (PV generation with power distribution networks leads to many operational challenges and complexities. Unintentional islanding is one of them which is of rising concern given the steady increase in grid-connected PV power. This paper builds up on an exploratory study of unintentional islanding on a modeled radial feeder having large PV penetration. Dynamic simulations, also run in real time, resulted in exploration of unique potential causes of creation of accidental islands. The resulting voltage and current data underwent dimensionality reduction using principal component analysis (PCA which formed the basis for the application of Q statistic control charts for detecting the anomalous currents that could island the system. For reducing the false alarm rate of anomaly detection, Kullback-Leibler (K-L divergence was applied on the principal component projections which concluded that Q statistic based approach alone is not reliable for detection of the symptoms liable to cause unintentional islanding. The obtained data was labeled and a K-nearest neighbor (K-NN binomial classifier was then trained for identification and classification of potential islanding precursors from other power system transients. The three-phase short-circuit fault case was successfully identified as statistically different from islanding symptoms.

  16. Characterizing and locating air pollution sources in a complex industrial district using optical remote sensing technology and multivariate statistical modeling.

    Science.gov (United States)

    Chang, Pao-Erh Paul; Yang, Jen-Chih Rena; Den, Walter; Wu, Chang-Fu

    2014-09-01

    Emissions of volatile organic compounds (VOCs) are most frequent environmental nuisance complaints in urban areas, especially where industrial districts are nearby. Unfortunately, identifying the responsible emission sources of VOCs is essentially a difficult task. In this study, we proposed a dynamic approach to gradually confine the location of potential VOC emission sources in an industrial complex, by combining multi-path open-path Fourier transform infrared spectrometry (OP-FTIR) measurement and the statistical method of principal component analysis (PCA). Close-cell FTIR was further used to verify the VOC emission source by measuring emitted VOCs from selected exhaust stacks at factories in the confined areas. Multiple open-path monitoring lines were deployed during a 3-month monitoring campaign in a complex industrial district. The emission patterns were identified and locations of emissions were confined by the wind data collected simultaneously. N,N-Dimethyl formamide (DMF), 2-butanone, toluene, and ethyl acetate with mean concentrations of 80.0 ± 1.8, 34.5 ± 0.8, 103.7 ± 2.8, and 26.6 ± 0.7 ppbv, respectively, were identified as the major VOC mixture at all times of the day around the receptor site. As the toxic air pollutant, the concentrations of DMF in air samples were found exceeding the ambient standard despite the path-average effect of OP-FTIR upon concentration levels. The PCA data identified three major emission sources, including PU coating, chemical packaging, and lithographic printing industries. Applying instrumental measurement and statistical modeling, this study has established a systematic approach for locating emission sources. Statistical modeling (PCA) plays an important role in reducing dimensionality of a large measured dataset and identifying underlying emission sources. Instrumental measurement, however, helps verify the outcomes of the statistical modeling. The field study has demonstrated the feasibility of

  17. Use of multivariate statistical tool for data processing in the analysis of Cu, Cr, Fe, Pb, Mo and Mg in lubricating oil by LIBS

    International Nuclear Information System (INIS)

    Alves, Luana F.N.; Sarkis, Jorge E.S.; Bordon, Isabela C.A.C.

    2015-01-01

    Analysis of industrial lubricants is widely used for monitoring and predicting maintenance requirements in a broad range of mechanical systems. Laser induced breakdown spectroscopy has been used to evaluate the potentiality of the technique for the determination of metals in lubricating oils. Prior to quantitative analysis, the LIBS system was calibrated using standard samples containing the elements investigated (Cu, Cr, Fe, Pb, Mo and Mg). This study presents the usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets in order to get more information about concentration of metals in oils lubricants is related to engine wear. (author)

  18. Application of multivariate statistical methods to classify archaeological pottery from Tel-Alramad site, Syria, based on x-ray fluorescence analysis

    International Nuclear Information System (INIS)

    Bakraji, E. H.

    2007-01-01

    Radioisotopic x-ray fluorescence (XRF) analysis has been utilized to determine the elemental composition of 55 archaeological pottery samples by the determination of 17 chemical elements. Fifty-four of them came from the Tel-Alramad Site in Katana town, near Damascus city, Syria, and one sample came from Brazil. The XRF results have been processed using two multivariate statistical methods, cluster and factor analysis, in order to determine similarities and correlation between the selected samples based on their elemental composition. The methodology successfully separates the samples where four distinct chemical groups were identified. (author)

  19. Using the expected detection delay to assess the performance of different multivariate statistical process monitoring methods for multiplicative and drift faults.

    Science.gov (United States)

    Zhang, Kai; Shardt, Yuri A W; Chen, Zhiwen; Peng, Kaixiang

    2017-03-01

    Using the expected detection delay (EDD) index to measure the performance of multivariate statistical process monitoring (MSPM) methods for constant additive faults have been recently developed. This paper, based on a statistical investigation of the T 2 - and Q-test statistics, extends the EDD index to the multiplicative and drift fault cases. As well, it is used to assess the performance of common MSPM methods that adopt these two test statistics. Based on how to use the measurement space, these methods can be divided into two groups, those which consider the complete measurement space, for example, principal component analysis-based methods, and those which only consider some subspace that reflects changes in key performance indicators, such as partial least squares-based methods. Furthermore, a generic form for them to use T 2 - and Q-test statistics are given. With the extended EDD index, the performance of these methods to detect drift and multiplicative faults is assessed using both numerical simulations and the Tennessee Eastman process. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  20. Assessing the hydrogeochemical processes affecting groundwater pollution in arid areas using an integration of geochemical equilibrium and multivariate statistical techniques

    International Nuclear Information System (INIS)

    El Alfy, Mohamed; Lashin, Aref; Abdalla, Fathy; Al-Bassam, Abdulaziz

    2017-01-01

    Rapid economic expansion poses serious problems for groundwater resources in arid areas, which typically have high rates of groundwater depletion. In this study, integration of hydrochemical investigations involving chemical and statistical analyses are conducted to assess the factors controlling hydrochemistry and potential pollution in an arid region. Fifty-four groundwater samples were collected from the Dhurma aquifer in Saudi Arabia, and twenty-one physicochemical variables were examined for each sample. Spatial patterns of salinity and nitrate were mapped using fitted variograms. The nitrate spatial distribution shows that nitrate pollution is a persistent problem affecting a wide area of the aquifer. The hydrochemical investigations and cluster analysis reveal four significant clusters of groundwater zones. Five main factors were extracted, which explain >77% of the total data variance. These factors indicated that the chemical characteristics of the groundwater were influenced by rock–water interactions and anthropogenic factors. The identified clusters and factors were validated with hydrochemical investigations. The geogenic factors include the dissolution of various minerals (calcite, aragonite, gypsum, anhydrite, halite and fluorite) and ion exchange processes. The anthropogenic factors include the impact of irrigation return flows and the application of potassium, nitrate, and phosphate fertilizers. Over time, these anthropogenic factors will most likely contribute to further declines in groundwater quality. - Highlights: • Hydrochemical investigations were carried out in Dhurma aquifer in Saudi Arabia. • The factors controlling potential groundwater pollution in an arid region were studied. • Chemical and statistical analyses are integrated to assess these factors. • Five main factors were extracted, which explain >77% of the total data variance. • The chemical characteristics of the groundwater were influenced by rock–water interactions

  1. Multivariate statistical process control (MSPC) using Raman spectroscopy for in-line culture cell monitoring considering time-varying batches synchronized with correlation optimized warping (COW).

    Science.gov (United States)

    Liu, Ya-Juan; André, Silvère; Saint Cristau, Lydia; Lagresle, Sylvain; Hannas, Zahia; Calvosa, Éric; Devos, Olivier; Duponchel, Ludovic

    2017-02-01

    Multivariate statistical process control (MSPC) is increasingly popular as the challenge provided by large multivariate datasets from analytical instruments such as Raman spectroscopy for the monitoring of complex cell cultures in the biopharmaceutical industry. However, Raman spectroscopy for in-line monitoring often produces unsynchronized data sets, resulting in time-varying batches. Moreover, unsynchronized data sets are common for cell culture monitoring because spectroscopic measurements are generally recorded in an alternate way, with more than one optical probe parallelly connecting to the same spectrometer. Synchronized batches are prerequisite for the application of multivariate analysis such as multi-way principal component analysis (MPCA) for the MSPC monitoring. Correlation optimized warping (COW) is a popular method for data alignment with satisfactory performance; however, it has never been applied to synchronize acquisition time of spectroscopic datasets in MSPC application before. In this paper we propose, for the first time, to use the method of COW to synchronize batches with varying durations analyzed with Raman spectroscopy. In a second step, we developed MPCA models at different time intervals based on the normal operation condition (NOC) batches synchronized by COW. New batches are finally projected considering the corresponding MPCA model. We monitored the evolution of the batches using two multivariate control charts based on Hotelling's T 2 and Q. As illustrated with results, the MSPC model was able to identify abnormal operation condition including contaminated batches which is of prime importance in cell culture monitoring We proved that Raman-based MSPC monitoring can be used to diagnose batches deviating from the normal condition, with higher efficacy than traditional diagnosis, which would save time and money in the biopharmaceutical industry. Copyright © 2016 Elsevier B.V. All rights reserved.

  2. Statistically based uncertainty analysis for ranking of component importance in the thermal-hydraulic safety analysis of the Advanced Neutron Source Reactor

    International Nuclear Information System (INIS)

    Wilson, G.E.

    1992-01-01

    The Analytic Hierarchy Process (AHP) has been used to help determine the importance of components and phenomena in thermal-hydraulic safety analyses of nuclear reactors. The AHP results are based, in part on expert opinion. Therefore, it is prudent to evaluate the uncertainty of the AHP ranks of importance. Prior applications have addressed uncertainty with experimental data comparisons and bounding sensitivity calculations. These methods work well when a sufficient experimental data base exists to justify the comparisons. However, in the case of limited or no experimental data the size of the uncertainty is normally made conservatively large. Accordingly, the author has taken another approach, that of performing a statistically based uncertainty analysis. The new work is based on prior evaluations of the importance of components and phenomena in the thermal-hydraulic safety analysis of the Advanced Neutron Source Reactor (ANSR), a new facility now in the design phase. The uncertainty during large break loss of coolant, and decay heat removal scenarios is estimated by assigning a probability distribution function (pdf) to the potential error in the initial expert estimates of pair-wise importance between the components. Using a Monte Carlo sampling technique, the error pdfs are propagated through the AHP software solutions to determine a pdf of uncertainty in the system wide importance of each component. To enhance the generality of the results, study of one other problem having different number of elements is reported, as are the effects of a larger assumed pdf error in the expert ranks. Validation of the Monte Carlo sample size and repeatability are also documented

  3. Data mining using multivariate statistical analysis: The case of heavy metals in sediments of the Msimbazi Creek mangrove wetland

    Directory of Open Access Journals (Sweden)

    A. Mrutu

    2013-12-01

    Full Text Available Mangrove wetlands are important biological systems that usually filter out organic and inorganic contaminants from the wastewaters before entering the ocean. Our previous work showed that sediments of the Msimbazi Creek wetland are contaminated with heavy metals and the amounts decreased with increasing depth. However, the hidden relationships between the heavy metals and clay particles were not fully understood based on the numerical data. Therefore this work used the data from literature and the Statistical Package for Social Sciences (SPSS software to study how significant the relationships are and predict the sources of heavy metals and clays. The results showed that Cd is the only metal that showed insignificant correlations with other heavy metals (with Pb and Zn while the rest of heavy metals exhibited significant positive correlation (except Pb vs. Ni. Cluster analysis classified the heavy metals based on the concentration and the first 50 cm cores (0-50 cm had higher heavy metals and % clay than the second 50 cm cores (51-100 cm. The results from the factor analysis suggests that Pb, Cd, Ni, and clay owe their source mostly from anthropogenic activities while Fe, Co, Cr, Zn and sand come from both anthropogenic and natural sources. These results support our previous suggestions that heavy metals and clays found in this wetland have mostly anthropogenic origin. However, we recommend isotopic tracing studies in order to accurately identify the origins of the heavy metals and clays in sediments of Msimbazi Creek mangrove wetland.

  4. Assessing the hydrogeochemical processes affecting groundwater pollution in arid areas using an integration of geochemical equilibrium and multivariate statistical techniques.

    Science.gov (United States)

    El Alfy, Mohamed; Lashin, Aref; Abdalla, Fathy; Al-Bassam, Abdulaziz

    2017-10-01

    Rapid economic expansion poses serious problems for groundwater resources in arid areas, which typically have high rates of groundwater depletion. In this study, integration of hydrochemical investigations involving chemical and statistical analyses are conducted to assess the factors controlling hydrochemistry and potential pollution in an arid region. Fifty-four groundwater samples were collected from the Dhurma aquifer in Saudi Arabia, and twenty-one physicochemical variables were examined for each sample. Spatial patterns of salinity and nitrate were mapped using fitted variograms. The nitrate spatial distribution shows that nitrate pollution is a persistent problem affecting a wide area of the aquifer. The hydrochemical investigations and cluster analysis reveal four significant clusters of groundwater zones. Five main factors were extracted, which explain >77% of the total data variance. These factors indicated that the chemical characteristics of the groundwater were influenced by rock-water interactions and anthropogenic factors. The identified clusters and factors were validated with hydrochemical investigations. The geogenic factors include the dissolution of various minerals (calcite, aragonite, gypsum, anhydrite, halite and fluorite) and ion exchange processes. The anthropogenic factors include the impact of irrigation return flows and the application of potassium, nitrate, and phosphate fertilizers. Over time, these anthropogenic factors will most likely contribute to further declines in groundwater quality. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Statistical control process to compare and rank treatment plans in radiation oncology: impact of heterogeneity correction on treatment planning in lung cancer.

    Science.gov (United States)

    Chaikh, Abdulhamid; Balosso, Jacques

    2016-12-01

    This study proposes a statistical process to compare different treatment plans issued from different irradiation techniques or different treatment phases. This approach aims to provide arguments for discussion about the impact on clinical results of any condition able to significantly alter dosimetric or ballistic related data. The principles of the statistical investigation are presented in the framework of a clinical example based on 40 fields of radiotherapy for lung cancers. Two treatment plans were generated for each patient making a change of dose distribution due to variation of lung density correction. The data from 2D gamma index (γ) including the pixels having γ≤1 were used to determine the capability index (Cp) and the acceptability index (Cpk) of the process. To measure the strength of the relationship between the γ passing rates and the Cp and Cpk indices, the Spearman's rank non-parametric test was used to calculate P values. The comparison between reference and tested plans showed that 95% of pixels have γ≤1 with criteria (6%, 6 mm). The values of the Cp and Cpk indices were lower than one showing a significant dose difference. The data showed a strong correlation between γ passing rates and the indices with P>0.8. The statistical analysis using Cp and Cpk, show the significance of dose differences resulting from two plans in radiotherapy. These indices can be used for adaptive radiotherapy to measure the difference between initial plan and daily delivered plan. The significant changes of dose distribution could raise the question about the continuity to treat the patient with the initial plan or the need for adjustments.

  6. Metal contamination in urban, suburban, and country park soils of Hong Kong: A study based on GIS and multivariate statistics

    International Nuclear Information System (INIS)

    Lee, Celine Siu-lan; Li Xiangdong; Shi Wenzhong; Cheung, Sharon Ching-nga; Thornton, Iain

    2006-01-01

    The urban environment quality is of vital importance as the majority of people now live in cities. Due to the continuous urbanisation and industrialisation in many parts of the world, metals are continuously emitted into the terrestrial environment and pose a great threat on human health. An extensive survey was conducted in the highly urbanised and commercialised Hong Kong Island area (80.3 km 2 ) of Hong Kong using a systematic sampling strategy of five soil samples per km 2 in urban areas and two samples per km 2 in the suburban and country park sites (0-15 cm). The analytical results indicated that the surface soils in urban and suburban areas are enriched with metals, such as Cu, Pb, and Zn. The Pb concentration in the urban soils was found to exceed the Dutch target value. The statistical analyses using principal component analysis (PCA) and cluster analysis (CA) showed distinctly different associations among trace metals and the major elements (Al, Ca, Fe, Mg, Mn) in the urban, suburban, and country park soils. Soil pollution maps of trace metals (Cd, Co, Cr, Cu, Ni, Pb, and Zn) in the surface soils were produced based on geographical information system (GIS) technology. The hot-spot areas of metal contamination were mainly concentrated in the northern and western parts of Hong Kong Island, and closely related to high traffic conditions. The Pb isotopic composition of the urban, suburban, and country park soils showed that vehicular emissions were the major anthropogenic sources for Pb. The 206 Pb/ 207 Pb and 208 Pb/ 207 Pb ratios in soils decreased as Pb concentrations increased in a polynomial line (degree = 2)

  7. Assessment of Groundwater Quality of Udayagiri area, Nellore District, Andhra Pradesh, South India Using Multivariate Statistical Techniques

    Directory of Open Access Journals (Sweden)

    Arveti Nagaraju

    2016-10-01

    Full Text Available Hydrogeochemical studies were carried out in and around Udayagiri area of Andhra Pradesh in order to assess the chemistry of the groundwater and to identify the dominant hydrogeochemical processes and mechanisms responsible for the evolution of the chemical composition of the groundwater. Descriptive statistics, correlation matrices, principal component analysis (PCA, together with cluster analysis (CA were used to gain an understanding of the hydrogeochemical processes in the study area. PCA has identified 4 main processes influencing the groundwater chemistry viz., mineral precipitation and dissolution, seawater intrusion, cation exchange, and carbonate balance. Further, three clusters C1, C2 and C3 were obtained. Samples from C1 contain high level of Cl− and may be due to the intensive evaporation and contamination from landfill leachate. Most of the samples from C2 are located closer to the sea and the high level of Na+ +K+ in these samples may be attributed to seawater intrusion. The geochemistry of water samples in C3 are more likely to originate from rock weathering. This has been supported by Gibbs diagram. The groundwater geochemistry in the study area is mostly of natural origin, but is influenced to some degree by human activity.    Evaluación de la calidad del agua subterránea a través de técnicas estadísticas multivariadas en el área Udayagiri, distrito Nellore, Andhra Pradesh, en el sur de India Resumen Se realizaron estudios hidrogeoquímicos en y alrededor del área Udayagiri de Andhra Pradesh para evaluar la química del agua subterránea e identificar los procesos hidrogeoquímicos dominantes y los mecanismos responsables de la evolución en la composición química del agua subterránea. Se utilizaron estadísticas descriptivas, matrices de correlación, análisis de componentes principales, al igual que análisis de grupos, para obtener y entender los procesos hidrogeoquímicos en el área de estudio. Los an

  8. Multivariate analysis with LISREL

    CERN Document Server

    Jöreskog, Karl G; Y Wallentin, Fan

    2016-01-01

    This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.

  9. Ranking as parameter estimation

    Czech Academy of Sciences Publication Activity Database

    Kárný, Miroslav; Guy, Tatiana Valentine

    2009-01-01

    Roč. 4, č. 2 (2009), s. 142-158 ISSN 1745-7645 R&D Projects: GA MŠk 2C06001; GA AV ČR 1ET100750401; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : ranking * Bayesian estimation * negotiation * modelling Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2009/AS/karny- ranking as parameter estimation.pdf

  10. Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

    Science.gov (United States)

    Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

    2017-05-01

    The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for

  11. Geochemistry of natural and anthropogenic fall-out (aerosol and precipitation) collected from the NW Mediterranean: two different multivariate statistical approaches

    International Nuclear Information System (INIS)

    Molinaroli, E.; Pistolato, M.; Rampazzo, G.; Guerzoni, S.

    1999-01-01

    The chemical characteristics of the mineral fractions of aerosol and precipitation collected in Sardinia (NW Mediterranean) are highlighted by means of two multivariate statistical approaches. Two different combinations of classification and statistical methods for geochemical data are presented. It is shown that the application of cluster analysis subsequent to Q-Factor analysis better distinguishes among Saharan dust, background pollution (Europe-Mediterranean) and local aerosol from various source regions (Sardinia). Conversely, the application of simple cluster analysis was able to distinguish only between aerosols and precipitation particles, without assigning the sources (local or distant) to the aerosol. This method also highlighted the fact that crust-enriched precipitation is similar to desert-derived aerosol. Major elements (Al, Na) and trace metal (Pb) turn out to be the most discriminating elements of the analysed data set. Independent use of mineralogical, granulometric and meteorological data confirmed the results derived from the statistical methods employed. (Copyright (c) 1999 Elsevier Science B.V., Amsterdam. All rights reserved.)

  12. Multivariate Analysis, Mass Balance Techniques, and Statistical Tests as Tools in Igneous Petrology: Application to the Sierra de las Cruces Volcanic Range (Mexican Volcanic Belt)

    Science.gov (United States)

    Velasco-Tapia, Fernando

    2014-01-01

    Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures). PMID:24737994

  13. Multivariate Analysis, Mass Balance Techniques, and Statistical Tests as Tools in Igneous Petrology: Application to the Sierra de las Cruces Volcanic Range (Mexican Volcanic Belt

    Directory of Open Access Journals (Sweden)

    Fernando Velasco-Tapia

    2014-01-01

    Full Text Available Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC volcanic range (Mexican Volcanic Belt. In this locality, the volcanic activity (3.7 to 0.5 Ma was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward’s linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas in the comingled lavas (binary mixtures.

  14. Inferring the origin of rare fruit distillates from compositional data using multivariate statistical analyses and the identification of new flavour constituents.

    Science.gov (United States)

    Mihajilov-Krstev, Tatjana M; Denić, Marija S; Zlatković, Bojan K; Stankov-Jovanović, Vesna P; Mitić, Violeta D; Stojanović, Gordana S; Radulović, Niko S

    2015-04-01

    In Serbia, delicatessen fruit alcoholic drinks are produced from autochthonous fruit-bearing species such as cornelian cherry, blackberry, elderberry, wild strawberry, European wild apple, European blueberry and blackthorn fruits. There are no chemical data on many of these and herein we analysed volatile minor constituents of these rare fruit distillates. Our second goal was to determine possible chemical markers of these distillates through a statistical/multivariate treatment of the herein obtained and previously reported data. Detailed chemical analyses revealed a complex volatile profile of all studied fruit distillates with 371 identified compounds. A number of constituents were recognised as marker compounds for a particular distillate. Moreover, 33 of them represent newly detected flavour constituents in alcoholic beverages or, in general, in foodstuffs. With the aid of multivariate analyses, these volatile profiles were successfully exploited to infer the origin of raw materials used in the production of these spirits. It was also shown that all fruit distillates possessed weak antimicrobial properties. It seems that the aroma of these highly esteemed wild-fruit spirits depends on the subtle balance of various minor volatile compounds, whereby some of them are specific to a certain type of fruit distillate and enable their mutual distinction. © 2014 Society of Chemical Industry.

  15. Discrimination of source reactor type by multivariate statistical analysis of uranium and plutonium isotopic concentrations in unknown irradiated nuclear fuel material.

    Science.gov (United States)

    Robel, Martin; Kristo, Michael J

    2008-11-01

    The problem of identifying the provenance of unknown nuclear material in the environment by multivariate statistical analysis of its uranium and/or plutonium isotopic composition is considered. Such material can be introduced into the environment as a result of nuclear accidents, inadvertent processing losses, illegal dumping of waste, or deliberate trafficking in nuclear materials. Various combinations of reactor type and fuel composition were analyzed using Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLSDA) of the concentrations of nine U and Pu isotopes in fuel as a function of burnup. Real-world variation in the concentrations of (234)U and (236)U in the fresh (unirradiated) fuel was incorporated. The U and Pu were also analyzed separately, with results that suggest that, even after reprocessing or environmental fractionation, Pu isotopes can be used to determine both the source reactor type and the initial fuel composition with good discrimination.

  16. Dissolution comparisons using a Multivariate Statistical Distance (MSD) test and a comparison of various approaches for calculating the measurements of dissolution profile comparison.

    Science.gov (United States)

    Cardot, J-M; Roudier, B; Schütz, H

    2017-07-01

    The f 2 test is generally used for comparing dissolution profiles. In cases of high variability, the f 2 test is not applicable, and the Multivariate Statistical Distance (MSD) test is frequently proposed as an alternative by the FDA and EMA. The guidelines provide only general recommendations. MSD tests can be performed either on raw data with or without time as a variable or on parameters of models. In addition, data can be limited-as in the case of the f 2 test-to dissolutions of up to 85% or to all available data. In the context of the present paper, the recommended calculation included all raw dissolution data up to the first point greater than 85% as a variable-without the various times as parameters. The proposed MSD overcomes several drawbacks found in other methods.

  17. Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam.

    Science.gov (United States)

    Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Dwirahmadi, Febi; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Do, Cuong Manh; Nguyen, Trung Hieu; Dinh, Tuan Anh Diep

    2015-05-01

    The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH₃ are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO₂ are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.

  18. Nitrate source identification in groundwater of multiple land-use areas by combining isotopes and multivariate statistical analysis: A case study of Asopos basin (Central Greece).

    Science.gov (United States)

    Matiatos, Ioannis

    2016-01-15

    Nitrate (NO3) is one of the most common contaminants in aquatic environments and groundwater. Nitrate concentrations and environmental isotope data (δ(15)N-NO3 and δ(18)O-NO3) from groundwater of Asopos basin, which has different land-use types, i.e., a large number of industries (e.g., textile, metal processing, food, fertilizers, paint), urban and agricultural areas and livestock breeding facilities, were analyzed to identify the nitrate sources of water contamination and N-biogeochemical transformations. A Bayesian isotope mixing model (SIAR) and multivariate statistical analysis of hydrochemical data were used to estimate the proportional contribution of different NO3 sources and to identify the dominant factors controlling the nitrate content of the groundwater in the region. The comparison of SIAR and Principal Component Analysis showed that wastes originating from urban and industrial zones of the basin are mainly responsible for nitrate contamination of groundwater in these areas. Agricultural fertilizers and manure likely contribute to groundwater contamination away from urban fabric and industrial land-use areas. Soil contribution to nitrate contamination due to organic matter is higher in the south-western part of the area far from the industries and the urban settlements. The present study aims to highlight the use of environmental isotopes combined with multivariate statistical analysis in locating sources of nitrate contamination in groundwater leading to a more effective planning of environmental measures and remediation strategies in river basins and water bodies as defined by the European Water Frame Directive (Directive 2000/60/EC).

  19. Nitrate source identification in groundwater of multiple land-use areas by combining isotopes and multivariate statistical analysis: A case study of Asopos basin (Central Greece)

    International Nuclear Information System (INIS)

    Matiatos, Ioannis

    2016-01-01

    Nitrate (NO_3) is one of the most common contaminants in aquatic environments and groundwater. Nitrate concentrations and environmental isotope data (δ"1"5N–NO_3 and δ"1"8O–NO_3) from groundwater of Asopos basin, which has different land-use types, i.e., a large number of industries (e.g., textile, metal processing, food, fertilizers, paint), urban and agricultural areas and livestock breeding facilities, were analyzed to identify the nitrate sources of water contamination and N-biogeochemical transformations. A Bayesian isotope mixing model (SIAR) and multivariate statistical analysis of hydrochemical data were used to estimate the proportional contribution of different NO_3 sources and to identify the dominant factors controlling the nitrate content of the groundwater in the region. The comparison of SIAR and Principal Component Analysis showed that wastes originating from urban and industrial zones of the basin are mainly responsible for nitrate contamination of groundwater in these areas. Agricultural fertilizers and manure likely contribute to groundwater contamination away from urban fabric and industrial land-use areas. Soil contribution to nitrate contamination due to organic matter is higher in the south-western part of the area far from the industries and the urban settlements. The present study aims to highlight the use of environmental isotopes combined with multivariate statistical analysis in locating sources of nitrate contamination in groundwater leading to a more effective planning of environmental measures and remediation strategies in river basins and water bodies as defined by the European Water Frame Directive (Directive 2000/60/EC). - Highlights: • More enriched N-isotope values were observed in the industrial/urban areas. • A Bayesian isotope mixing model was applied in a multiple land-use area. • A 3-component model explained the factors controlling nitrate content in groundwater. • Industrial/urban nitrogen source was

  20. Robust multivariate analysis

    CERN Document Server

    J Olive, David

    2017-01-01

    This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given.  The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory.   The robust techniques  are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis.  A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with...

  1. Dating and classification of Syrian excavated pottery from Tell Saka Site, by means of thermoluminescence analysis, and multivariate statistical methods, based on PIXE analysis

    International Nuclear Information System (INIS)

    Bakraji, E.H.; Ahmad, M.; Salman, N.; Haloum, D.; Boutros, N.; Abboud, R.

    2011-01-01

    Thermoluminescence (TL) dating and Proton Induced X-ray Emission (PIXE) techniques have been utilized for the study of archaeological pottery fragment samples from Tell Saka Site, which is located at 25 km south east of Damascus city, Syria. Four samples were chosen randomly from the site, two from third level and two from fourth level for dating using TL technique and the results were in good agreement with the date assigned by archaeologists. Twenty-eight sherds were analyzed using PIXE technique in order to identify and characterize the elemental composition of pottery excavated from third and fourth levels, using 3 MV tandem accelerator in Damascus. The analysis provided almost 20 elements (Na, Mg, Al, Si, P, S, K, Ca, Ti, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Y, Zr, Nb). However, only 14 elements as follows: K, Ca, Ti, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Y, Zr, Nb were chosen for statistical analysis and have been processed using two multivariate statistical methods, Cluster and Factor analysis. The studied pottery were classify into two well defined groups. (author)

  2. Chemometric and multivariate statistical analysis of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulfides.

    Science.gov (United States)

    Kalegowda, Yogesh; Harmer, Sarah L

    2012-03-20

    Time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of mineral samples are complex, comprised of large mass ranges and many peaks. Consequently, characterization and classification analysis of these systems is challenging. In this study, different chemometric and statistical data evaluation methods, based on monolayer sensitive TOF-SIMS data, have been tested for the characterization and classification of copper-iron sulfide minerals (chalcopyrite, chalcocite, bornite, and pyrite) at different flotation pulp conditions (feed, conditioned feed, and Eh modified). The complex mass spectral data sets were analyzed using the following chemometric and statistical techniques: principal component analysis (PCA); principal component-discriminant functional analysis (PC-DFA); soft independent modeling of class analogy (SIMCA); and k-Nearest Neighbor (k-NN) classification. PCA was found to be an important first step in multivariate analysis, providing insight into both the relative grouping of samples and the elemental/molecular basis for those groupings. For samples exposed to oxidative conditions (at Eh ~430 mV), each technique (PCA, PC-DFA, SIMCA, and k-NN) was found to produce excellent classification. For samples at reductive conditions (at Eh ~ -200 mV SHE), k-NN and SIMCA produced the most accurate classification. Phase identification of particles that contain the same elements but a different crystal structure in a mixed multimetal mineral system has been achieved.

  3. Identifying sources of soil inorganic pollutants on a regional scale using a multivariate statistical approach: Role of pollutant migration and soil physicochemical properties

    International Nuclear Information System (INIS)

    Zhang Changbo; Wu Longhua; Luo Yongming; Zhang Haibo; Christie, Peter

    2008-01-01

    Principal components analysis (PCA) and correlation analysis were used to estimate the contribution of four components related to pollutant sources on the total variation in concentrations of Cu, Zn, Pb, Cd, As, Se, Hg, Fe and Mn in surface soil samples from a valley in east China with numerous copper and zinc smelters. Results indicate that when carrying out source identification of inorganic pollutants their tendency to migrate in soils may result in differences between the pollutant composition of the source and the receptor soil, potentially leading to errors in the characterization of pollutants using multivariate statistics. The stability and potential migration or movement of pollutants in soils must therefore be taken into account. Soil physicochemical properties may offer additional useful information. Two different mechanisms have been hypothesized for correlations between soil heavy metal concentrations and soil organic matter content and these may be helpful in interpreting the statistical analysis. - Principal components analysis with Varimax rotation can help identify sources of soil inorganic pollutants but pollutant migration and soil properties can exert important effects

  4. Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques.

    Science.gov (United States)

    Gao, Yongnian; Gao, Junfeng; Yin, Hongbin; Liu, Chuansheng; Xia, Ting; Wang, Jing; Huang, Qi

    2015-03-15

    Remote sensing has been widely used for ater quality monitoring, but most of these monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating the phosphorus concentration in water. The total phosphorus (TP) in lakes has been estimated from remotely sensed observations, primarily using the simple individual band ratio or their natural logarithm and the statistical regression method based on the field TP data and the spectral reflectance. In this study, we investigated the possibility of establishing a spatial modeling scheme to estimate the TP concentration of a large lake from multi-spectral satellite imagery using band combinations and regional multivariate statistical modeling techniques, and we tested the applicability of the spatial modeling scheme. The results showed that HJ-1A CCD multi-spectral satellite imagery can be used to estimate the TP concentration in a lake. The correlation and regression analysis showed a highly significant positive relationship between the TP concentration and certain remotely sensed combination variables. The proposed modeling scheme had a higher accuracy for the TP concentration estimation in the large lake compared with the traditional individual band ratio method and the whole-lake scale regression-modeling scheme. The TP concentration values showed a clear spatial variability and were high in western Lake Chaohu and relatively low in eastern Lake Chaohu. The northernmost portion, the northeastern coastal zone and the southeastern portion of western Lake Chaohu had the highest TP concentrations, and the other regions had the lowest TP concentration values, except for the coastal zone of eastern Lake Chaohu. These results strongly suggested that the proposed modeling scheme, i.e., the band combinations and the regional multivariate

  5. Nitrate source identification in groundwater of multiple land-use areas by combining isotopes and multivariate statistical analysis: A case study of Asopos basin (Central Greece)

    Energy Technology Data Exchange (ETDEWEB)

    Matiatos, Ioannis, E-mail: i.matiatos@iaea.org

    2016-01-15

    Nitrate (NO{sub 3}) is one of the most common contaminants in aquatic environments and groundwater. Nitrate concentrations and environmental isotope data (δ{sup 15}N–NO{sub 3} and δ{sup 18}O–NO{sub 3}) from groundwater of Asopos basin, which has different land-use types, i.e., a large number of industries (e.g., textile, metal processing, food, fertilizers, paint), urban and agricultural areas and livestock breeding facilities, were analyzed to identify the nitrate sources of water contamination and N-biogeochemical transformations. A Bayesian isotope mixing model (SIAR) and multivariate statistical analysis of hydrochemical data were used to estimate the proportional contribution of different NO{sub 3} sources and to identify the dominant factors controlling the nitrate content of the groundwater in the region. The comparison of SIAR and Principal Component Analysis showed that wastes originating from urban and industrial zones of the basin are mainly responsible for nitrate contamination of groundwater in these areas. Agricultural fertilizers and manure likely contribute to groundwater contamination away from urban fabric and industrial land-use areas. Soil contribution to nitrate contamination due to organic matter is higher in the south-western part of the area far from the industries and the urban settlements. The present study aims to highlight the use of environmental isotopes combined with multivariate statistical analysis in locating sources of nitrate contamination in groundwater leading to a more effective planning of environmental measures and remediation strategies in river basins and water bodies as defined by the European Water Frame Directive (Directive 2000/60/EC). - Highlights: • More enriched N-isotope values were observed in the industrial/urban areas. • A Bayesian isotope mixing model was applied in a multiple land-use area. • A 3-component model explained the factors controlling nitrate content in groundwater. • Industrial

  6. Joint density of eigenvalues in spiked multivariate models.

    Science.gov (United States)

    Dharmawansa, Prathapasinghe; Johnstone, Iain M

    2014-01-01

    The classical methods of multivariate analysis are based on the eigenvalues of one or two sample covariance matrices. In many applications of these methods, for example to high dimensional data, it is natural to consider alternative hypotheses which are a low rank departure from the null hypothesis. For rank one alternatives, this note provides a representation for the joint eigenvalue density in terms of a single contour integral. This will be of use for deriving approximate distributions for likelihood ratios and 'linear' statistics used in testing.

  7. Multivariate statistical monitoring as applied to clean-in-place (CIP) and steam-in-place (SIP) operations in biopharmaceutical manufacturing.

    Science.gov (United States)

    Roy, Kevin; Undey, Cenk; Mistretta, Thomas; Naugle, Gregory; Sodhi, Manbir

    2014-01-01

    Multivariate statistical process monitoring (MSPM) is becoming increasingly utilized to further enhance process monitoring in the biopharmaceutical industry. MSPM can play a critical role when there are many measurements and these measurements are highly correlated, as is typical for many biopharmaceutical operations. Specifically, for processes such as cleaning-in-place (CIP) and steaming-in-place (SIP, also known as sterilization-in-place), control systems typically oversee the execution of the cycles, and verification of the outcome is based on offline assays. These offline assays add to delays and corrective actions may require additional setup times. Moreover, this conventional approach does not take interactive effects of process variables into account and cycle optimization opportunities as well as salient trends in the process may be missed. Therefore, more proactive and holistic online continued verification approaches are desirable. This article demonstrates the application of real-time MSPM to processes such as CIP and SIP with industrial examples. The proposed approach has significant potential for facilitating enhanced continuous verification, improved process understanding, abnormal situation detection, and predictive monitoring, as applied to CIP and SIP operations. © 2014 American Institute of Chemical Engineers.

  8. Study of Syrian archaeological pottery by the combined application of thermoluminescence (TL) dating, X-ray fluorescence analysis and statistical multivariate analysis

    International Nuclear Information System (INIS)

    Bakraji, E.H.

    2012-01-01

    X-ray fluorescence method and the technique of thermoluminescence (TL) dating have been utilized for the study of archaeological pottery fragment samples, fairly representative of Romanian period between 1 st century B.C. and 4th century A.D, from Judaidet Yabous site, which is located north-west of Damascus city, Syria. Four samples were chosen randomly among the forty six samples for dating using thermoluminescence technique and the results were in good agreement with the date assigned by archaeologists. The samples were irradiated for 1000 s live time twice, first using a Mo X-ray Tube and second using a 109 Cd radioactive source. Fifteen elements (K, Ca, Ti, Mn, Fe, Ni, Cu, Zn, Ga, Rb, Sr, Y, Zr, Nb, and Pb) were determined. The elemental concentrations have been processed using two multivariate statistical methods. The purpose of the study was to characterize by means of elements contents the pottery paste from Judaidet Yabous archaeological site and providing new data to the Syrian databases for future studies. From an archaeological point of view the results indicated that most of the potteries, were locally produced. (author)

  9. PIXE multivariate statistics and OSL investigation for the classification and dating of archaeological pottery excavated at Tell Al-Rawda site, Syria

    Energy Technology Data Exchange (ETDEWEB)

    Bakraji, E.H., E-mail: cscientificl@aec.org.sy [Archaeometry Laboratory, Chemistry Department, Atomic Energy Commission of Syria, P. O. Box 6091, Damascus (Syrian Arab Republic); Rihawy, M.S. [Archaeometry Laboratory, Chemistry Department, Atomic Energy Commission of Syria, P. O. Box 6091, Damascus (Syrian Arab Republic); Castel, C. [CNRS – Maison de l’Orient et de la Méditerranée, Laboratoire “Archéorient”, CNRS/Université Lumière-Lyon 2 (France); Abboud, R. [Archaeometry Laboratory, Chemistry Department, Atomic Energy Commission of Syria, P. O. Box 6091, Damascus (Syrian Arab Republic)

    2015-03-15

    Highlights: •PIXE and OSL methods were used to classify and date pottery from Tell Al-Rawda site. •Three groups were classified using PIXE, which suggest different sources of the clay. •OSL was used for dating the site and the date found was consistent with typology. -- Abstract: Particle Induced X-ray Emission (PIXE) technique has been utilised to study 48 Syrian ancient pottery fragments taken from excavations at Tell Al-Rawda site. Eighteen elements (Mg, Al, Si, P, S, K, Ca, Ti, Mn, Fe, Ni, Zn, As, Br, Rb, Sr, Y, and Pb) were determined. The elements concentrations have been processed using two multivariate statistical methods, to classify the pottery where one main group and other two small groups were defined. In addition, four samples from different places on the site were subjected to optically stimulated luminescence (OSL) dating. The average age obtained using a single aliquot regeneration (SAR) protocol was found to be 4350 ± 240 year.

  10. Characterization and discrimination of raw and vinegar-baked Bupleuri radix based on UHPLC-Q-TOF-MS coupled with multivariate statistical analysis.

    Science.gov (United States)

    Lei, Tianli; Chen, Shifeng; Wang, Kai; Zhang, Dandan; Dong, Lin; Lv, Chongning; Wang, Jing; Lu, Jincai

    2018-02-01

    Bupleuri Radix is a commonly used herb in clinic, and raw and vinegar-baked Bupleuri Radix are both documented in the Pharmacopoeia of People's Republic of China. According to the theories of traditional Chinese medicine, Bupleuri Radix possesses different therapeutic effects before and after processing. However, the chemical mechanism of this processing is still unknown. In this study, ultra-high-performance liquid chromatography with quadruple time-of-flight mass spectrometry coupled with multivariate statistical analysis including principal component analysis and orthogonal partial least square-discriminant analysis was developed to holistically compare the difference between raw and vinegar-baked Bupleuri Radix for the first time. As a result, 50 peaks in raw and processed Bupleuri Radix were detected, respectively, and a total of 49 peak chemical compounds were identified. Saikosaponin a, saikosaponin d, saikosaponin b 3 , saikosaponin e, saikosaponin c, saikosaponin b 2 , saikosaponin b 1 , 4''-O-acetyl-saikosaponin d, hyperoside and 3',4'-dimethoxy quercetin were explored as potential markers of raw and vinegar-baked Bupleuri Radix. This study has been successfully applied for global analysis of raw and vinegar-processed samples. Furthermore, the underlying hepatoprotective mechanism of Bupleuri Radix was predicted, which was related to the changes of chemical profiling. Copyright © 2017 John Wiley & Sons, Ltd.

  11. Comparing Relationships among Yield and Its Related Traits in Mycorrhizal and Nonmycorrhizal Inoculated Wheat Cultivars under Different Water Regimes Using Multivariate Statistics

    Directory of Open Access Journals (Sweden)

    Armin Saed-Moucheshi

    2013-01-01

    Full Text Available Multivariate statistical techniques were used to compare the relationship between yield and its related traits under noninoculated and inoculated cultivars with mycorrhizal fungus (Glomus intraradices; each one consisted of three wheat cultivars and four water regimes. Results showed that, under inoculation conditions, spike weight per plant and total chlorophyll content of the flag leaf were the most important variables contributing to wheat grain yield variation, while, under noninoculated condition, in addition to two mentioned traits, grain weight per spike and leaf area were also important variables accounting for wheat grain yield variation. Therefore, spike weight per plant and chlorophyll content of flag leaf can be used as selection criteria in breeding programs for both inoculated and noninoculated wheat cultivars under different water regimes, and also grain weight per spike and leaf area can be considered for noninoculated condition. Furthermore, inoculation of wheat cultivars showed higher value in the most measured traits, and the results indicated that inoculation treatment could change the relationship among morphological traits of wheat cultivars under drought stress. Also, it seems that the results of stepwise regression as a selecting method together with principal component and factor analysis are stronger methods to be applied in breeding programs for screening important traits.

  12. A standards-based method for compositional analysis by energy dispersive X-ray spectrometry using multivariate statistical analysis: application to multicomponent alloys.

    Science.gov (United States)

    Rathi, Monika; Ahrenkiel, S P; Carapella, J J; Wanlass, M W

    2013-02-01

    Given an unknown multicomponent alloy, and a set of standard compounds or alloys of known composition, can one improve upon popular standards-based methods for energy dispersive X-ray (EDX) spectrometry to quantify the elemental composition of the unknown specimen? A method is presented here for determining elemental composition of alloys using transmission electron microscopy-based EDX with appropriate standards. The method begins with a discrete set of related reference standards of known composition, applies multivariate statistical analysis to those spectra, and evaluates the compositions with a linear matrix algebra method to relate the spectra to elemental composition. By using associated standards, only limited assumptions about the physical origins of the EDX spectra are needed. Spectral absorption corrections can be performed by providing an estimate of the foil thickness of one or more reference standards. The technique was applied to III-V multicomponent alloy thin films: composition and foil thickness were determined for various III-V alloys. The results were then validated by comparing with X-ray diffraction and photoluminescence analysis, demonstrating accuracy of approximately 1% in atomic fraction.

  13. Rapid differentiation of Chinese hop varieties (Humulus lupulus) using volatile fingerprinting by HS-SPME-GC-MS combined with multivariate statistical analysis.

    Science.gov (United States)

    Liu, Zechang; Wang, Liping; Liu, Yumei

    2018-01-18

    Hops impart flavor to beer, with the volatile components characterizing the various hop varieties and qualities. Fingerprinting, especially flavor fingerprinting, is often used to identify 'flavor products' because inconsistencies in the description of flavor may lead to an incorrect definition of beer quality. Compared to flavor fingerprinting, volatile fingerprinting is simpler and easier. We performed volatile fingerprinting using head space-solid phase micro-extraction gas chromatography-mass spectrometry combined with similarity analysis and principal component analysis (PCA) for evaluating and distinguishing between three major Chinese hops. Eighty-four volatiles were identified, which were classified into seven categories. Volatile fingerprinting based on similarity analysis did not yield any obvious result. By contrast, hop varieties and qualities were identified using volatile fingerprinting based on PCA. The potential variables explained the variance in the three hop varieties. In addition, the dendrogram and principal component score plot described the differences and classifications of hops. Volatile fingerprinting plus multivariate statistical analysis can rapidly differentiate between the different varieties and qualities of the three major Chinese hops. Furthermore, this method can be used as a reference in other fields. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

  14. Estrogenic compound profiles in an urbanized industry-impacted coastal bay and potential risk assessment by pollution indices and multivariative statistical methods.

    Science.gov (United States)

    Wang, Zaosheng; Li, Rui; Wu, Fengchang; Feng, Chenglian; Ye, Chun; Yan, Changzhou

    2017-01-15

    The occurrence and distribution of target estrogenic compounds in a highly urbanized industry-impacted coastal bay were investigated, and contamination profiles were evaluated by estimating total estradiol equivalents (∑EEQs) and risk quotients (RQs). Phenolic compounds were the most abundant xenoestrogens, but seldom showed contribution to the ∑EEQs. The diethylstilbestrol (DES) and 17α-ethinylestradiol (EE2) were the major contributors followed by 17β-estradiol (E2) in comparison with a slight contribution from estrone (E1) and estriol (E3). Both ∑EEQs and RQs indicated likely adverse effects posed on resident organisms. Further, multivariate statistical method comprehensively revealed pollution status by visualized factor scores and identified multiple "hotspots" of estrogenic sources, demonstrating the presence of complex pollution risk gradients inside and particularly outside of bay area. Overall, this study favors the integrative utilization of pollution indices and factor analysis as powerful tool to scientifically diagnose the pollution characterization of human-derived chemicals for better management decisions in aquatic environments. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Classification of the medicinal plants of the genus Atractylodes using high-performance liquid chromatography with diode array and tandem mass spectrometry detection combined with multivariate statistical analysis.

    Science.gov (United States)

    Cho, Hyun-Deok; Kim, Unyong; Suh, Joon Hyuk; Eom, Han Young; Kim, Junghyun; Lee, Seul Gi; Choi, Yong Seok; Han, Sang Beom

    2016-04-01

    Analytical methods using high-performance liquid chromatography with diode array and tandem mass spectrometry detection were developed for the discrimination of the rhizomes of four Atractylodes medicinal plants: A. japonica, A. macrocephala, A. chinensis, and A. lancea. A quantitative study was performed, selecting five bioactive components, including atractylenolide I, II, III, eudesma-4(14),7(11)-dien-8-one and atractylodin, on twenty-six Atractylodes samples of various origins. Sample extraction was optimized to sonication with 80% methanol for 40 min at room temperature. High-performance liquid chromatography with diode array detection was established using a C18 column with a water/acetonitrile gradient system at a flow rate of 1.0 mL/min, and the detection wavelength was set at 236 nm. Liquid chromatography with tandem mass spectrometry was applied to certify the reliability of the quantitative results. The developed methods were validated by ensuring specificity, linearity, limit of quantification, accuracy, precision, recovery, robustness, and stability. Results showed that cangzhu contained higher amounts of atractylenolide I and atractylodin than baizhu, and especially atractylodin contents showed the greatest variation between baizhu and cangzhu. Multivariate statistical analysis, such as principal component analysis and hierarchical cluster analysis, were also employed for further classification of the Atractylodes plants. The established method was suitable for quality control of the Atractylodes plants. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  16. Human Exposure Risk Assessment Due to Heavy Metals in Groundwater by Pollution Index and Multivariate Statistical Methods: A Case Study from South Africa

    Directory of Open Access Journals (Sweden)

    Vetrimurugan Elumalai

    2017-04-01

    Full Text Available Heavy metals in surface and groundwater were analysed and their sources were identified using multivariate statistical tools for two towns in South Africa. Human exposure risk through the drinking water pathway was also assessed. Electrical conductivity values showed that groundwater is desirable to permissible for drinking except for six locations. Concentration of aluminium, lead and nickel were above the permissible limit for drinking at all locations. Boron, cadmium, iron and manganese exceeded the limit at few locations. Heavy metal pollution index based on ten heavy metals indicated that 85% of the area had good quality water, but 15% was unsuitable. Human exposure dose through the drinking water pathway indicated no risk due to boron, nickel and zinc, moderate risk due to cadmium and lithium and high risk due to silver, copper, manganese and lead. Hazard quotients were high in all sampling locations for humans of all age groups, indicating that groundwater is unsuitable for drinking purposes. Highly polluted areas were located near the coast, close to industrial operations and at a landfill site representing human-induced pollution. Factor analysis identified the four major pollution sources as: (1 industries; (2 mining and related activities; (3 mixed sources- geogenic and anthropogenic and (4 fertilizer application.

  17. The interprocess NIR sampling as an alternative approach to multivariate statistical process control for identifying sources of product-quality variability.

    Science.gov (United States)

    Marković, Snežana; Kerč, Janez; Horvat, Matej

    2017-03-01

    We are presenting a new approach of identifying sources of variability within a manufacturing process by NIR measurements of samples of intermediate material after each consecutive unit operation (interprocess NIR sampling technique). In addition, we summarize the development of a multivariate statistical process control (MSPC) model for the production of enteric-coated pellet product of the proton-pump inhibitor class. By developing provisional NIR calibration models, the identification of critical process points yields comparable results to the established MSPC modeling procedure. Both approaches are shown to lead to the same conclusion, identifying parameters of extrusion/spheronization and characteristics of lactose that have the greatest influence on the end-product's enteric coating performance. The proposed approach enables quicker and easier identification of variability sources during manufacturing process, especially in cases when historical process data is not straightforwardly available. In the presented case the changes of lactose characteristics are influencing the performance of the extrusion/spheronization process step. The pellet cores produced by using one (considered as less suitable) lactose source were on average larger and more fragile, leading to consequent breakage of the cores during subsequent fluid bed operations. These results were confirmed by additional experimental analyses illuminating the underlying mechanism of fracture of oblong pellets during the pellet coating process leading to compromised film coating.

  18. Elemental characterization of herbal medicines used in Ghana by instrumental neutron activation analysis and atomic absorption spectrometry and multivariate statistical analysis

    International Nuclear Information System (INIS)

    Ayivor, J.E.; Nyarko, B.J.B.; Dampare, S.B.; Okine, L.K.

    2010-01-01

    k 0 instrumental neutron activation analysis and atomic absorption spectrometry were applied to determine multi elements in thirteen Ghanaian herbal medicines used for the management of various diseases. Concentrations of AI, Cu, Mg, Mn and Na were determined. As, Br, K, CI, and Na were determined by short and medium irradiations at a thermal neutron flux of 5x10ncm -2 s -1 . Fe, Cr, Pb, Co, Ni, Sn, Ca, Ba, Li and Sb were determined using atomic absorption spectrometry. Ba, Cu, Li and V were present at trace levels whereas AI, CI, Na, Ca were present at major levels. K, Br, Mg, Mn, Co, Ni, Fe and Sb were also present at minor levels. The precision and accuracy of the method using real samples and standard reference materials were within ±10% of the reported value. Multivariate analytical techniques, such as cluster analysis and principal component analysis (PCA)/factor analysis (FA), have been applied to evaluate the chemical variations in the herbal medicine dataset. All the 13 samples may be grouped into two statistically significant clusters, reflecting the different chemical compositions. The concentrations of elements were within the recommended daily allowances or maximum permissible levels posing no adverse effects on human health.

  19. Metabolomic profiling of the phytomedicinal constituents of Carica papaya L. leaves and seeds by 1H NMR spectroscopy and multivariate statistical analysis.

    Science.gov (United States)

    Gogna, Navdeep; Hamid, Neda; Dorai, Kavita

    2015-11-10

    Extracts from the Carica papaya L. plant are widely reported to contain metabolites with antibacterial, antioxidant and anticancer activity. This study aims to analyze the metabolic profiles of papaya leaves and seeds in order to gain insights into their phytomedicinal constituents. We performed metabolite fingerprinting using 1D and 2D 1H NMR experiments and used multivariate statistical analysis to identify those plant parts that contain the most concentrations of metabolites of phytomedicinal value. Secondary metabolites such as phenyl propanoids, including flavonoids, were found in greater concentrations in the leaves as compared to the seeds. UPLC-ESI-MS verified the presence of significant metabolites in the papaya extracts suggested by the NMR analysis. Interestingly, the concentration of eleven secondary metabolites namely caffeic, cinnamic, chlorogenic, quinic, coumaric, vanillic, and protocatechuic acids, naringenin, hesperidin, rutin, and kaempferol, were higher in young as compared to old papaya leaves. The results of the NMR analysis were corroborated by estimating the total phenolic and flavonoid content of the extracts. Estimation of antioxidant activity in leaves and seed extracts by DPPH and ABTS in-vitro assays and antioxidant capacity in C2C12 cell line also showed that papaya extracts exhibit high antioxidant activity. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. PIXE multivariate statistics and OSL investigation for the classification and dating of archaeological pottery excavated at Tell Al-Rawda site, Syria

    International Nuclear Information System (INIS)

    Bakraji, E.H.; Rihawy, M.S.; Castel, C.; Abboud, R.

    2015-01-01

    Highlights: •PIXE and OSL methods were used to classify and date pottery from Tell Al-Rawda site. •Three groups were classified using PIXE, which suggest different sources of the clay. •OSL was used for dating the site and the date found was consistent with typology. -- Abstract: Particle Induced X-ray Emission (PIXE) technique has been utilised to study 48 Syrian ancient pottery fragments taken from excavations at Tell Al-Rawda site. Eighteen elements (Mg, Al, Si, P, S, K, Ca, Ti, Mn, Fe, Ni, Zn, As, Br, Rb, Sr, Y, and Pb) were determined. The elements concentrations have been processed using two multivariate statistical methods, to classify the pottery where one main group and other two small groups were defined. In addition, four samples from different places on the site were subjected to optically stimulated luminescence (OSL) dating. The average age obtained using a single aliquot regeneration (SAR) protocol was found to be 4350 ± 240 year

  1. Generalized reduced rank tests using the singular value decomposition

    NARCIS (Netherlands)

    Kleibergen, F.R.; Paap, R.

    2002-01-01

    We propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: necessity of a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson (1951), sensitivity to the ordering of the variables for the LDU

  2. Generalized Reduced Rank Tests using the Singular Value Decomposition

    NARCIS (Netherlands)

    F.R. Kleibergen (Frank); R. Paap (Richard)

    2003-01-01

    textabstractWe propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: necessity of a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson (1951), sensitivity to the ordering of the variables

  3. University Rankings: The Web Ranking

    Science.gov (United States)

    Aguillo, Isidro F.

    2012-01-01

    The publication in 2003 of the Ranking of Universities by Jiao Tong University of Shanghai has revolutionized not only academic studies on Higher Education, but has also had an important impact on the national policies and the individual strategies of the sector. The work gathers the main characteristics of this and other global university…

  4. Statistics

    CERN Document Server

    Hayslett, H T

    1991-01-01

    Statistics covers the basic principles of Statistics. The book starts by tackling the importance and the two kinds of statistics; the presentation of sample data; the definition, illustration and explanation of several measures of location; and the measures of variation. The text then discusses elementary probability, the normal distribution and the normal approximation to the binomial. Testing of statistical hypotheses and tests of hypotheses about the theoretical proportion of successes in a binomial population and about the theoretical mean of a normal population are explained. The text the

  5. Multivariate data analysis

    DEFF Research Database (Denmark)

    Hansen, Michael Adsetts Edberg

    Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...

  6. Statistics

    Science.gov (United States)

    Links to sources of cancer-related statistics, including the Surveillance, Epidemiology and End Results (SEER) Program, SEER-Medicare datasets, cancer survivor prevalence data, and the Cancer Trends Progress Report.

  7. Hydro-geochemical paths of multi-layer groundwater system in coal mining regions - Using multivariate statistics and geochemical modeling approaches.

    Science.gov (United States)

    Liu, Pu; Hoth, Nils; Drebenstedt, Carsten; Sun, Yajun; Xu, Zhimin

    2017-12-01

    Groundwater is an important drinking water resource that requires protection in North China. Coal mining industry in the area may influence the water quality evolution. To provide primary characterization of the hydrogeochemical processes and paths that control the water quality evolution, a complex multi-layer groundwater system in a coal mining area is investigated. Multivariate statistical methods involving hierarchical cluster analysis (HCA) and principal component analysis (PCA) are applied, 6 zones and 3 new principal components are classified as major reaction zones and reaction factors. By integrating HCA and PCA with hydrogeochemical correlations analysis, potential phases, reactions and connections between various zones are presented. Carbonates minerals, gypsum, clay minerals as well as atmosphere gases - CO 2 , H 2 O and NH 3 are recognized as major reactants. Mixtures, evaporation, dissolution/precipitation of minerals and cation exchange are potential reactions. Inverse modeling is finally used, and it verifies the detailed processes and diverse paths. Consequently, 4 major paths are found controlling the variations of groundwater chemical properties. Shallow and deep groundwater is connected primarily by the flow of deep groundwater up through fractures and faults into the shallow aquifers. Mining does not impact the underlying aquifers that represent the most critical groundwater resource. But controls should be taken to block the mixing processes from highly polluted mine water. The paper highlights the complex hydrogeochemical evolution of a multi-layer groundwater system under mining impact, which could be applied to further groundwater quality management in the study area, as well as most of the other coalfields in North China. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Simultaneous Analysis of Saccharides between Fresh and Processed Radix Rehmanniae by HPLC and UHPLC-LTQ-Orbitrap-MS with Multivariate Statistical Analysis

    Directory of Open Access Journals (Sweden)

    Shujuan Xue

    2018-02-01

    Full Text Available Radix Rehmanniae (RR is a kind of herb which is widely used in the clinical and food processing industry. There are four forms of RR used in traditional Chinese medicine practice, which include fresh RR (FRR, raw RR (RRR, processed RR (PRR, and another processed RR (APRR, in which the APRR was processed by nine cycles of repeated steaming and drying. There are a large number of saccharides in RR. However, the differences in content were shown by different processing methods. In this study, an effective method using high-performance liquid chromatography (HPLC and high-performance liquid chromatography-mass spectrometry (LC-MS coupled with multivariate statistical analysis to rapidly distinguish different RR samples and validate the proposed chemical conversion mechanism. The datasets of the content of saccharides were subjected to principal component analysis (PCA and one-way analysis of variance. The results showed that there different changes occurred in the contents of saccharides corresponding to the different processing methods, in which the contents of monosaccharides—namely arabinose, glucose, mannose, and galactose—had an increasing trend or remained relatively stable. However, the contents of fructose and oligosaccharides, including manninotriose, melibiose, sucrose, and raffinose, first increased and then reduced, or gradually decreased, yet the content of stachyose gradually decreased. The MSn data indicated that manninotriose, melibiose, and some monosaccharides were produced by the hydrolysis of oligosaccharides. In addition, the fragmentation pathways of 1-phenyl-3-methyl-5-pyrazolone (PMP derivatization of monosaccharides were also found that its glycosidic bond was first broken and subsequently its inside ring broke, and the characteristic fragment ions were produced at m/z 511.22, 493.20, 373.16, and 175.08 in the PMP derivatization of monosaccharides. In conclusion, this study illustrates the change and chemical conversion

  9. Basin Characterisation by Means of Joint Inversion of Electromagnetic Geophysical Data, Borehole Data and Multivariate Statistical Methods: The Loop Head Peninsula, Western Ireland, Case Study

    Science.gov (United States)

    Campanya, J. L.; Ogaya, X.; Jones, A. G.; Rath, V.; McConnell, B.; Haughton, P.; Prada, M.

    2016-12-01

    The Science Foundation Ireland funded project IRECCSEM project (www.ireccsem.ie) aims to evaluate Ireland's potential for onshore carbon sequestration in saline aquifers by integrating new electromagnetic geophysical data with existing geophysical and geological data. One of the objectives of this component of IRECCSEM is to characterise the subsurface beneath the Loop Head Peninsula (part of Clare Basin, Co. Clare, Ireland), and identify major electrical resistivity structures that can guide an interpretation of the carbon sequestration potential of this area. During the summer of 2014, a magnetotelluric (MT) survey was carried out on the Loop Head Peninsula, and data from a total of 140 sites were acquired, including audio-magnetotelluric (AMT), and broadband magnetotelluric (BBMT). The dataset was used to generate shallow three-dimensional (3-D) electrical resistivity models constraining the subsurface to depths of up to 3.5 km. The three-dimensional (3-D) joint inversions were performed using three different types of electromagnetic data: MT impedance tensor (Z), geomagnetic transfer functions (T), and inter-station horizontal magnetic transfer-functions (H). The interpretation of the results was complemented with second-derivative models of the resulting electrical resistivity models, and a quantitative comparison with borehole data using multivariate statistical methods. Second-derivative models were used to define the main interfaces between the geoelectrical structures, facilitating superior comparison with geological and seismic results, and also reducing the influence of the colour scale when interpreting the results. Specific analysis was performed to compare the extant borehole data with the electrical resistivity model, identifying those structures that are better characterised by the resistivity model. Finally, the electrical resistivity model was also used to propagate some of the physical properties measured in the borehole, when a good relation was

  10. Influence of physical and chemical characteristics of diesel fuels and exhaust emissions on biological effects of particle extracts: a multivariate statistical analysis of ten diesel fuels.

    Science.gov (United States)

    Sjögren, M; Li, H; Banner, C; Rafter, J; Westerholm, R; Rannug, U

    1996-01-01

    The emission of diesel exhaust particulates is associated with potentially severe biological effects, e.g., cancer. The aim of the present study was to apply multivariate statistical methods to identify factors that affect the biological potency of these exhausts. Ten diesel fuels were analyzed regarding physical and chemical characteristics. Particulate exhaust emissions were sampled after combustion of these fuels on two makes of heavy duty diesel engines. Particle extracts were chemically analyzed and tested for mutagenicity in the Ames test. Also, the potency of the extracts to competitively inhibit the binding of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) to the Ah receptor was assessed. Relationships between fuel characteristics and biological effects of the extracts were studied, using partial least squares regression (PLS). The most influential chemical fuel parameters included the contents of sulfur, certain polycyclic aromatic compounds (PAC), and naphthenes. Density and flash point were positively correlated with genotoxic potency. Cetane number and upper distillation curve points were negatively correlated with both mutagenicity and Ah receptor affinity. Between 61% and 70% of the biological response data could be explained by the measured chemical and physical factors of the fuels. By PLS modeling of extract data versus the biological response data, 66% of the genotoxicity could be explained, by 41% of the chemical variation. The most important variables, associated with both mutagenicity and Ah receptor affinity, included 1-nitropyrene, particle bound nitrate, indeno[1,2,3-cd]pyrene, and emitted mass of particles. S9-requiring mutagenicity was highly correlated with certain PAC, whereas S9-independent mutagenicity was better correlated with nitrates and 1-nitropyrene. The emission of sulfates also showed a correlation both with the emission of particles and with the biological effects. The results indicate that fuels with biologically less hazardous

  11. Hydrogeochemistry and quality of surface water and groundwater in the vicinity of Lake Monoun, West Cameroon: approach from multivariate statistical analysis and stable isotopic characterization.

    Science.gov (United States)

    Kamtchueng, Brice T; Fantong, Wilson Y; Wirmvem, Mengnjo J; Tiodjio, Rosine E; Takounjou, Alain F; Ndam Ngoupayou, Jules R; Kusakabe, Minoru; Zhang, Jing; Ohba, Takeshi; Tanyileke, Gregory; Hell, Joseph V; Ueda, Akira

    2016-09-01

    With the use of conventional hydrogeochemical techniques, multivariate statistical analysis, and stable isotope approaches, this paper investigates for the first time surface water and groundwater from the surrounding areas of Lake Monoun (LM), West Cameroon. The results reveal that waters are generally slightly acidic to neutral. The relative abundance of major dissolved species are Ca(2+) > Mg(2+) > Na(+) > K(+) for cations and HCO3 (-) ≫ NO3 (-) > Cl(-) > SO4 (2-) for anions. The main water type is Ca-Mg-HCO3. Observed salinity is related to water-rock interaction, ion exchange process, and anthropogenic activities. Nitrate and chloride have been identified as the most common pollutants. These pollutants are attributed to the chlorination of wells and leaching from pit latrines and refuse dumps. The stable isotopic compositions in the investigated water sources suggest evidence of evaporation before recharge. Four major groups of waters were identified by salinity and NO3 concentrations using the Q-mode hierarchical cluster analysis (HCA). Consistent with the isotopic results, group 1 represents fresh unpolluted water occurring near the recharge zone in the general flow regime; groups 2 and 3 are mixed water whose composition is controlled by both weathering of rock-forming minerals and anthropogenic activities; group 4 represents water under high vulnerability of anthropogenic pollution. Moreover, the isotopic results and the HCA showed that the CO2-rich bottom water of LM belongs to an isolated hydrological system within the Foumbot plain. Except for some springs, groundwater water in the area is inappropriate for drinking and domestic purposes but good to excellent for irrigation.

  12. Modeling the geochemical distribution of rare earth elements (REEs using multivariate statistics in the eastern part of Marvast placer, the Yazd province

    Directory of Open Access Journals (Sweden)

    Amin Hossein Morshedy

    2017-07-01

    Full Text Available Introduction Nowadays, exploration of rare earth element (REE resources is considered as one of the strategic priorities, which has a special position in the advanced and intelligent industries (Castor and Hedrick, 2006. Significant resources of REEs are found in a wide range of geological settings, including primary deposits associated with igneous and hydrothermal processes (e.g. carbonatite, (per alkaline-igneous rocks, iron-oxide breccia complexes, scarns, fluorapatite veins and pegmatites, and secondary deposits concentrated by sedimentary processes and weathering (e.g. heavy-mineral sand deposits, fluviatile sandstones, unconformity-related uranium deposits, and lignites (Jaireth et al., 2014. Recent studies on various parts of Iran led to the identification of promising potential of these elements, including Central Iran, alkaline rocks in the Eslami Peninsula, iron and apatite in the Hormuz Island, Kahnouj titanium deposit, granitoid bodies in Yazd, Azerbaijan, and Mashhad and associated dikes, and finally placers related to the Shemshak formation in Marvast, Kharanagh, and Ardekan indicate high concentration of REE in magmatogenic iron–apatite deposits in Central Iran and placers in Marvast area in Yazd (Ghorbani, 2013. Materials and methods In the present study, the geochemical behavior of rare earth elements is modeled by using multivariate statistical methods in the eastern part of the Marvast placer. Marvast is located 185 km south of the city of Yazd in central Iran between Yazd and Mehriz. This area lies within the southeastern part of the Sanandaj-Sirjan Zone (Alipour-Asll et al., 2012. The samples of 53 wells were analyzed for Whole-rock trace-element concentrations (including REE by inductively coupled plasma-mass spectrometry (ICP-MS (GSI, 2004. The clustering techniques such as multivariate statistical analysis technique can be employed to find appropriate groups in data sets. One of the main objectives of data clustering

  13. Statistics

    International Nuclear Information System (INIS)

    2005-01-01

    For the years 2004 and 2005 the figures shown in the tables of Energy Review are partly preliminary. The annual statistics published in Energy Review are presented in more detail in a publication called Energy Statistics that comes out yearly. Energy Statistics also includes historical time-series over a longer period of time (see e.g. Energy Statistics, Statistics Finland, Helsinki 2004.) The applied energy units and conversion coefficients are shown in the back cover of the Review. Explanatory notes to the statistical tables can be found after tables and figures. The figures presents: Changes in GDP, energy consumption and electricity consumption, Carbon dioxide emissions from fossile fuels use, Coal consumption, Consumption of natural gas, Peat consumption, Domestic oil deliveries, Import prices of oil, Consumer prices of principal oil products, Fuel prices in heat production, Fuel prices in electricity production, Price of electricity by type of consumer, Average monthly spot prices at the Nord pool power exchange, Total energy consumption by source and CO 2 -emissions, Supplies and total consumption of electricity GWh, Energy imports by country of origin in January-June 2003, Energy exports by recipient country in January-June 2003, Consumer prices of liquid fuels, Consumer prices of hard coal, natural gas and indigenous fuels, Price of natural gas by type of consumer, Price of electricity by type of consumer, Price of district heating by type of consumer, Excise taxes, value added taxes and fiscal charges and fees included in consumer prices of some energy sources and Energy taxes, precautionary stock fees and oil pollution fees

  14. Wikipedia ranking of world universities

    Science.gov (United States)

    Lages, José; Patt, Antoine; Shepelyansky, Dima L.

    2016-03-01

    We use the directed networks between articles of 24 Wikipedia language editions for producing the wikipedia ranking of world Universities (WRWU) using PageRank, 2DRank and CheiRank algorithms. This approach allows to incorporate various cultural views on world universities using the mathematical statistical analysis independent of cultural preferences. The Wikipedia ranking of top 100 universities provides about 60% overlap with the Shanghai university ranking demonstrating the reliable features of this approach. At the same time WRWU incorporates all knowledge accumulated at 24 Wikipedia editions giving stronger highlights for historically important universities leading to a different estimation of efficiency of world countries in university education. The historical development of university ranking is analyzed during ten centuries of their history.

  15. The association of 83 plasma proteins with CHD mortality, BMI, HDL-, and total-cholesterol in men: Applying multivariate statistics to identify proteins with prognostic value and biological relevance

    NARCIS (Netherlands)

    Geert Heidema, A.; Thissen, U.; Boer, J.M.A.; Bouwman, F.G.; Feskens, E.J.M.; Mariman, E.C.M.

    2009-01-01

    In this study, we applied the multivariate statistical tool Partial Least Squares (PLS) to analyze the relative importance of 83 plasma proteins in relation to coronary heart disease (CHD) mortality and the intermediate end points body mass index, HDL-cholesterol and total cholesterol. From a Dutch

  16. Statistics

    International Nuclear Information System (INIS)

    2001-01-01

    For the year 2000, part of the figures shown in the tables of the Energy Review are preliminary or estimated. The annual statistics of the Energy Review appear in more detail from the publication Energiatilastot - Energy Statistics issued annually, which also includes historical time series over a longer period (see e.g. Energiatilastot 1999, Statistics Finland, Helsinki 2000, ISSN 0785-3165). The inside of the Review's back cover shows the energy units and the conversion coefficients used for them. Explanatory notes to the statistical tables can be found after tables and figures. The figures presents: Changes in the volume of GNP and energy consumption, Changes in the volume of GNP and electricity, Coal consumption, Natural gas consumption, Peat consumption, Domestic oil deliveries, Import prices of oil, Consumer prices of principal oil products, Fuel prices for heat production, Fuel prices for electricity production, Carbon dioxide emissions from the use of fossil fuels, Total energy consumption by source and CO 2 -emissions, Electricity supply, Energy imports by country of origin in 2000, Energy exports by recipient country in 2000, Consumer prices of liquid fuels, Consumer prices of hard coal, natural gas and indigenous fuels, Average electricity price by type of consumer, Price of district heating by type of consumer, Excise taxes, value added taxes and fiscal charges and fees included in consumer prices of some energy sources and Energy taxes and precautionary stock fees on oil products

  17. Statistics

    International Nuclear Information System (INIS)

    2000-01-01

    For the year 1999 and 2000, part of the figures shown in the tables of the Energy Review are preliminary or estimated. The annual statistics of the Energy Review appear in more detail from the publication Energiatilastot - Energy Statistics issued annually, which also includes historical time series over a longer period (see e.g., Energiatilastot 1998, Statistics Finland, Helsinki 1999, ISSN 0785-3165). The inside of the Review's back cover shows the energy units and the conversion coefficients used for them. Explanatory notes to the statistical tables can be found after tables and figures. The figures presents: Changes in the volume of GNP and energy consumption, Changes in the volume of GNP and electricity, Coal consumption, Natural gas consumption, Peat consumption, Domestic oil deliveries, Import prices of oil, Consumer prices of principal oil products, Fuel prices for heat production, Fuel prices for electricity production, Carbon dioxide emissions, Total energy consumption by source and CO 2 -emissions, Electricity supply, Energy imports by country of origin in January-March 2000, Energy exports by recipient country in January-March 2000, Consumer prices of liquid fuels, Consumer prices of hard coal, natural gas and indigenous fuels, Average electricity price by type of consumer, Price of district heating by type of consumer, Excise taxes, value added taxes and fiscal charges and fees included in consumer prices of some energy sources and Energy taxes and precautionary stock fees on oil products

  18. Statistics

    International Nuclear Information System (INIS)

    1999-01-01

    For the year 1998 and the year 1999, part of the figures shown in the tables of the Energy Review are preliminary or estimated. The annual statistics of the Energy Review appear in more detail from the publication Energiatilastot - Energy Statistics issued annually, which also includes historical time series over a longer period (see e.g. Energiatilastot 1998, Statistics Finland, Helsinki 1999, ISSN 0785-3165). The inside of the Review's back cover shows the energy units and the conversion coefficients used for them. Explanatory notes to the statistical tables can be found after tables and figures. The figures presents: Changes in the volume of GNP and energy consumption, Changes in the volume of GNP and electricity, Coal consumption, Natural gas consumption, Peat consumption, Domestic oil deliveries, Import prices of oil, Consumer prices of principal oil products, Fuel prices for heat production, Fuel prices for electricity production, Carbon dioxide emissions, Total energy consumption by source and CO 2 -emissions, Electricity supply, Energy imports by country of origin in January-June 1999, Energy exports by recipient country in January-June 1999, Consumer prices of liquid fuels, Consumer prices of hard coal, natural gas and indigenous fuels, Average electricity price by type of consumer, Price of district heating by type of consumer, Excise taxes, value added taxes and fiscal charges and fees included in consumer prices of some energy sources and Energy taxes and precautionary stock fees on oil products

  19. Statistics

    International Nuclear Information System (INIS)

    2003-01-01

    For the year 2002, part of the figures shown in the tables of the Energy Review are partly preliminary. The annual statistics of the Energy Review also includes historical time-series over a longer period (see e.g. Energiatilastot 2001, Statistics Finland, Helsinki 2002). The applied energy units and conversion coefficients are shown in the inside back cover of the Review. Explanatory notes to the statistical tables can be found after tables and figures. The figures presents: Changes in GDP, energy consumption and electricity consumption, Carbon dioxide emissions from fossile fuels use, Coal consumption, Consumption of natural gas, Peat consumption, Domestic oil deliveries, Import prices of oil, Consumer prices of principal oil products, Fuel prices in heat production, Fuel prices in electricity production, Price of electricity by type of consumer, Average monthly spot prices at the Nord pool power exchange, Total energy consumption by source and CO 2 -emissions, Supply and total consumption of electricity GWh, Energy imports by country of origin in January-June 2003, Energy exports by recipient country in January-June 2003, Consumer prices of liquid fuels, Consumer prices of hard coal, natural gas and indigenous fuels, Price of natural gas by type of consumer, Price of electricity by type of consumer, Price of district heating by type of consumer, Excise taxes, value added taxes and fiscal charges and fees included in consumer prices of some energy sources and Excise taxes, precautionary stock fees on oil pollution fees on energy products

  20. Statistics

    International Nuclear Information System (INIS)

    2004-01-01

    For the year 2003 and 2004, the figures shown in the tables of the Energy Review are partly preliminary. The annual statistics of the Energy Review also includes historical time-series over a longer period (see e.g. Energiatilastot, Statistics Finland, Helsinki 2003, ISSN 0785-3165). The applied energy units and conversion coefficients are shown in the inside back cover of the Review. Explanatory notes to the statistical tables can be found after tables and figures. The figures presents: Changes in GDP, energy consumption and electricity consumption, Carbon dioxide emissions from fossile fuels use, Coal consumption, Consumption of natural gas, Peat consumption, Domestic oil deliveries, Import prices of oil, Consumer prices of principal oil products, Fuel prices in heat production, Fuel prices in electricity production, Price of electricity by type of consumer, Average monthly spot prices at the Nord pool power exchange, Total energy consumption by source and CO 2 -emissions, Supplies and total consumption of electricity GWh, Energy imports by country of origin in January-March 2004, Energy exports by recipient country in January-March 2004, Consumer prices of liquid fuels, Consumer prices of hard coal, natural gas and indigenous fuels, Price of natural gas by type of consumer, Price of electricity by type of consumer, Price of district heating by type of consumer, Excise taxes, value added taxes and fiscal charges and fees included in consumer prices of some energy sources and Excise taxes, precautionary stock fees on oil pollution fees

  1. Statistics

    International Nuclear Information System (INIS)

    2000-01-01

    For the year 1999 and 2000, part of the figures shown in the tables of the Energy Review are preliminary or estimated. The annual statistics of the Energy also includes historical time series over a longer period (see e.g., Energiatilastot 1999, Statistics Finland, Helsinki 2000, ISSN 0785-3165). The inside of the Review's back cover shows the energy units and the conversion coefficients used for them. Explanatory notes to the statistical tables can be found after tables and figures. The figures presents: Changes in the volume of GNP and energy consumption, Changes in the volume of GNP and electricity, Coal consumption, Natural gas consumption, Peat consumption, Domestic oil deliveries, Import prices of oil, Consumer prices of principal oil products, Fuel prices for heat production, Fuel prices for electricity production, Carbon dioxide emissions, Total energy consumption by source and CO 2 -emissions, Electricity supply, Energy imports by country of origin in January-June 2000, Energy exports by recipient country in January-June 2000, Consumer prices of liquid fuels, Consumer prices of hard coal, natural gas and indigenous fuels, Average electricity price by type of consumer, Price of district heating by type of consumer, Excise taxes, value added taxes and fiscal charges and fees included in consumer prices of some energy sources and Energy taxes and precautionary stock fees on oil products

  2. Time evolution of Wikipedia network ranking

    Science.gov (United States)

    Eom, Young-Ho; Frahm, Klaus M.; Benczúr, András; Shepelyansky, Dima L.

    2013-12-01

    We study the time evolution of ranking and spectral properties of the Google matrix of English Wikipedia hyperlink network during years 2003-2011. The statistical properties of ranking of Wikipedia articles via PageRank and CheiRank probabilities, as well as the matrix spectrum, are shown to be stabilized for 2007-2011. A special emphasis is done on ranking of Wikipedia personalities and universities. We show that PageRank selection is dominated by politicians while 2DRank, which combines PageRank and CheiRank, gives more accent on personalities of arts. The Wikipedia PageRank of universities recovers 80% of top universities of Shanghai ranking during the considered time period.

  3. Multivariate analysis methods in physics

    International Nuclear Information System (INIS)

    Wolter, M.

    2007-01-01

    A review of multivariate methods based on statistical training is given. Several multivariate methods useful in high-energy physics analysis are discussed. Selected examples from current research in particle physics are discussed, both from the on-line trigger selection and from the off-line analysis. Also statistical training methods are presented and some new application are suggested [ru

  4. A multivariate statistical approaches on physicochemical characteristics of ground water in and around Nagapattinam district, Cauvery deltaic region of Tamil Nadu, India

    Directory of Open Access Journals (Sweden)

    Venkatramanan Senapathi

    2013-07-01

    Full Text Available Ground water samples collected at different locations in and around the Nagapattinam district were analyzed for their physicochemical characteristics. The ground water samples were collected from fifty two dug and deep wells during the monsoon and summer seasons in June and December, 2011. The present investigation is focused on the determination of physico-chemical parameters such as pH, EC, TDS, Ca, Mg, Na, K, HCO3, SO4 and Cl. Ground water suitability for drinking, domestic and agri- cultural purposes was examined by using WHO standards. Correlation, factor and cluster analyses were applied to classify the ground water qualities and to categorize the geochemical processes controlling ground water geochemistry. Factor analysis indicates that seawater intrusion and agriculture runoff are dominant factors controlling the hydrogeochemistry of ground water in the study area. Cluster analysis was helpful for the classification on the basis of contamination characteristics of ground water quality. This study also elucidates that multivariate statistical analyses can be used to improve the understanding of ground water status and assessment of ground water quality.  Resumen En este estudio se analizan las características fisicoquímicas de muestras de aguas subterráneas tomadas en diferentes locaciones en y alrededor del distrito de Nagapattinam. Las muestras se recolectaron en 52 pozos cavados y perforaciones profundas durante las subestaciones del monzón y el verano, en los meses de junio y diciembre (2011. La presente investigación está enfocada en la determinación de parametros fisicoquímicos como pH, EC, TDS, Ca, Mg, Na, K, HCO3, SO4 y Cl. Se examinó la pertinencia de estas aguas para consumo y para irrigación a la luz de los estándares de la Organización Mundial de la Salud. Se aplicaron análisis de correlación, factores y cúmulos para clasificar las muestras y categorizar los procesos geoquímicos que controlan las aguas subterr

  5. Nontargeted, Rapid Screening of Extra Virgin Olive Oil Products for Authenticity Using Near-Infrared Spectroscopy in Combination with Conformity Index and Multivariate Statistical Analyses.

    Science.gov (United States)

    Karunathilaka, Sanjeewa R; Kia, Ali-Reza Fardin; Srigley, Cynthia; Chung, Jin Kyu; Mossoba, Magdi M

    2016-10-01

    A rapid tool for evaluating authenticity was developed and applied to the screening of extra virgin olive oil (EVOO) retail products by using Fourier-transform near infrared (FT-NIR) spectroscopy in combination with univariate and multivariate data analysis methods. Using disposable glass tubes, spectra for 62 reference EVOO, 10 edible oil adulterants, 20 blends consisting of EVOO spiked with adulterants, 88 retail EVOO products and other test samples were rapidly measured in the transmission mode without any sample preparation. The univariate conformity index (CI) and the multivariate supervised soft independent modeling of class analogy (SIMCA) classification tool were used to analyze the various olive oil products which were tested for authenticity against a library of reference EVOO. Better discrimination between the authentic EVOO and some commercial EVOO products was observed with SIMCA than with CI analysis. Approximately 61% of all EVOO commercial products were flagged by SIMCA analysis, suggesting that further analysis be performed to identify quality issues and/or potential adulterants. Due to its simplicity and speed, FT-NIR spectroscopy in combination with multivariate data analysis can be used as a complementary tool to conventional official methods of analysis to rapidly flag EVOO products that may not belong to the class of authentic EVOO. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

  6. Statistical energy as a tool for binning-free, multivariate goodness-of-fit tests, two-sample comparison and unfolding

    International Nuclear Information System (INIS)

    Aslan, B.; Zech, G.

    2005-01-01

    We introduce the novel concept of statistical energy as a statistical tool. We define statistical energy of statistical distributions in a similar way as for electric charge distributions. Charges of opposite sign are in a state of minimum energy if they are equally distributed. This property is used to check whether two samples belong to the same parent distribution, to define goodness-of-fit tests and to unfold distributions distorted by measurement. The approach is binning-free and especially powerful in multidimensional applications

  7. ArraySolver: An Algorithm for Colour-Coded Graphical Display and Wilcoxon Signed-Rank Statistics for Comparing Microarray Gene Expression Data

    OpenAIRE

    Khan, Haseeb Ahmad

    2004-01-01

    The massive surge in the production of microarray data poses a great challenge for proper analysis and interpretation. In recent years numerous computational tools have been developed to extract meaningful interpretation of microarray gene expression data. However, a convenient tool for two-groups comparison of microarray data is still lacking and users have to rely on commercial statistical packages that might be costly and require special skills, in addition to extra time and effort for tra...

  8. Hierarchical Decompositions for the Computation of High-Dimensional Multivariate Normal Probabilities

    KAUST Repository

    Genton, Marc G.

    2017-09-07

    We present a hierarchical decomposition scheme for computing the n-dimensional integral of multivariate normal probabilities that appear frequently in statistics. The scheme exploits the fact that the formally dense covariance matrix can be approximated by a matrix with a hierarchical low rank structure. It allows the reduction of the computational complexity per Monte Carlo sample from O(n2) to O(mn+knlog(n/m)), where k is the numerical rank of off-diagonal matrix blocks and m is the size of small diagonal blocks in the matrix that are not well-approximated by low rank factorizations and treated as dense submatrices. This hierarchical decomposition leads to substantial efficiencies in multivariate normal probability computations and allows integrations in thousands of dimensions to be practical on modern workstations.

  9. Hierarchical Decompositions for the Computation of High-Dimensional Multivariate Normal Probabilities

    KAUST Repository

    Genton, Marc G.; Keyes, David E.; Turkiyyah, George

    2017-01-01

    We present a hierarchical decomposition scheme for computing the n-dimensional integral of multivariate normal probabilities that appear frequently in statistics. The scheme exploits the fact that the formally dense covariance matrix can be approximated by a matrix with a hierarchical low rank structure. It allows the reduction of the computational complexity per Monte Carlo sample from O(n2) to O(mn+knlog(n/m)), where k is the numerical rank of off-diagonal matrix blocks and m is the size of small diagonal blocks in the matrix that are not well-approximated by low rank factorizations and treated as dense submatrices. This hierarchical decomposition leads to substantial efficiencies in multivariate normal probability computations and allows integrations in thousands of dimensions to be practical on modern workstations.

  10. Multivariate and spatial statistical analysis of Callovo-Oxfordian physical properties from lab and borehole logs data: towards a characterization of lateral and vertical spatial trends in the Meuse/Haute-Marne transposition zone

    International Nuclear Information System (INIS)

    Garcia, M.H.; Rabaute, A.; Yven, B.; Guillemot, D.

    2010-01-01

    relevant information about the spatial continuity of rock properties as measured on cores in laboratory. To do so, multivariate statistical analysis methods, including principal component analysis based on linear or rank (Spearman) correlations, were carried out. They show that well-log compressive velocity ( V p) is well correlated to static Young modulus and compressive strength measured on cores, and that downhole bulk density and Total CMR porosity are well correlated to dynamic Young modulus, dynamic shear modulus and compressive velocity on cores. Studying the spatial continuity and trends of properties in argillaceous units was a primary objective of the study. To do so, the spatial analysis was first conducted on the well-log properties that proved to be well correlated to properties measured on cores, lab properties remaining the reference physical properties. Lateral and vertical spatial trends were observed and interpreted on the selected well-log properties. In order to confirm that these spatial trends were effective and could apply to physical properties measured on cores, the spatial continuity of some correlated lab properties was studied. Similar trends were found that validated the approach of using log properties for characterizing the spatial continuity of core physical properties. (authors)

  11. Sparse structure regularized ranking

    KAUST Repository

    Wang, Jim Jing-Yan; Sun, Yijun; Gao, Xin

    2014-01-01

    Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse

  12. Multivariate analysis: models and method

    International Nuclear Information System (INIS)

    Sanz Perucha, J.

    1990-01-01

    Data treatment techniques are increasingly used since computer methods result of wider access. Multivariate analysis consists of a group of statistic methods that are applied to study objects or samples characterized by multiple values. A final goal is decision making. The paper describes the models and methods of multivariate analysis

  13. Multivariate pattern dependence.

    Directory of Open Access Journals (Sweden)

    Stefano Anzellotti

    2017-11-01

    Full Text Available When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD: a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS and to the fusiform face area (FFA, using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.

  14. Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques

    Science.gov (United States)

    Su, Shiliang; Zhi, Junjun; Lou, Liping; Huang, Fang; Chen, Xia; Wu, Jiaping

    Characterizing the spatio-temporal patterns and apportioning the pollution sources of water bodies are important for the management and protection of water resources. The main objective of this study is to describe the dynamics of water quality and provide references for improving river pollution control practices. Comprehensive application of neural-based modeling and different multivariate methods was used to evaluate the spatio-temporal patterns and source apportionment of pollution in Qiantang River, China. Measurement data were obtained and pretreated for 13 variables from 41 monitoring sites for the period of 2001-2004. A self-organizing map classified the 41 monitoring sites into three groups (Group A, B and C), representing different pollution characteristics. Four significant parameters (dissolved oxygen, biochemical oxygen demand, total phosphorus and total lead) were identified by discriminant analysis for distinguishing variations of different years, with about 80% correct assignment for temporal variation. Rotated principal component analysis (PCA) identified four potential pollution sources for Group A (domestic sewage and agricultural pollution, industrial wastewater pollution, mineral weathering, vehicle exhaust and sand mining), five for Group B (heavy metal pollution, agricultural runoff, vehicle exhaust and sand mining, mineral weathering, chemical plants discharge) and another five for Group C (vehicle exhaust and sand mining, chemical plants discharge, soil weathering, biochemical pollution, mineral weathering). The identified potential pollution sources explained 75.6% of the total variances for Group A, 75.0% for Group B and 80.0% for Group C, respectively. Receptor-based source apportionment was applied to further estimate source contributions for each pollution variable in the three groups, which facilitated and supported the PCA results. These results could assist managers to develop optimal strategies and determine priorities for river

  15. Determination of the archaeological origin of ceramic fragments characterized by neutron activation analysis, by means of the application of multivariable statistical analysis techniques

    International Nuclear Information System (INIS)

    Almazan T, M. G.; Jimenez R, M.; Monroy G, F.; Tenorio, D.; Rodriguez G, N. L.

    2009-01-01

    The elementary composition of archaeological ceramic fragments obtained during the explorations in San Miguel Ixtapan, Mexico State, was determined by the neutron activation analysis technique. The samples irradiation was realized in the research reactor TRIGA Mark III with a neutrons flow of 1·10 13 n·cm -2 ·s -1 . The irradiation time was of 2 hours. Previous to the acquisition of the gamma rays spectrum the samples were allowed to decay from 12 to 14 days. The analyzed elements were: Nd, Ce, Lu, Eu, Yb, Pa(Th), Tb, La, Cr, Hf, Sc, Co, Fe, Cs, Rb. The statistical treatment of the data, consistent in the group analysis and the main components analysis allowed to identify three different origins of the archaeological ceramic, designated as: local, foreign and regional. (Author)

  16. Chemical modeling of groundwater in the Banat Plain, southwestern Romania, with elevated As content and co-occurring species by combining diagrams and unsupervised multivariate statistical approaches.

    Science.gov (United States)

    Butaciu, Sinziana; Senila, Marin; Sarbu, Costel; Ponta, Michaela; Tanaselia, Claudiu; Cadar, Oana; Roman, Marius; Radu, Emil; Sima, Mihaela; Frentiu, Tiberiu

    2017-04-01

    The study proposes a combined model based on diagrams (Gibbs, Piper, Stuyfzand Hydrogeochemical Classification System) and unsupervised statistical approaches (Cluster Analysis, Principal Component Analysis, Fuzzy Principal Component Analysis, Fuzzy Hierarchical Cross-Clustering) to describe natural enrichment of inorganic arsenic and co-occurring species in groundwater in the Banat Plain, southwestern Romania. Speciation of inorganic As (arsenite, arsenate), ion concentrations (Na + , K + , Ca 2+ , Mg 2+ , HCO 3 - , Cl - , F - , SO 4 2- , PO 4 3- , NO 3 - ), pH, redox potential, conductivity and total dissolved substances were performed. Classical diagrams provided the hydrochemical characterization, while statistical approaches were helpful to establish (i) the mechanism of naturally occurring of As and F - species and the anthropogenic one for NO 3 - , SO 4 2- , PO 4 3- and K + and (ii) classification of groundwater based on content of arsenic species. The HCO 3 - type of local groundwater and alkaline pH (8.31-8.49) were found to be responsible for the enrichment of arsenic species and occurrence of F - but by different paths. The PO 4 3- -AsO 4 3- ion exchange, water-rock interaction (silicates hydrolysis and desorption from clay) were associated to arsenate enrichment in the oxidizing aquifer. Fuzzy Hierarchical Cross-Clustering was the strongest tool for the rapid simultaneous classification of groundwaters as a function of arsenic content and hydrogeochemical characteristics. The approach indicated the Na + -F - -pH cluster as marker for groundwater with naturally elevated As and highlighted which parameters need to be monitored. A chemical conceptual model illustrating the natural and anthropogenic paths and enrichment of As and co-occurring species in the local groundwater supported by mineralogical analysis of rocks was established. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. The association of 83 plasma proteins with CHD mortality, BMI, HDL-, and total-cholesterol in men: applying multivariate statistics to identify proteins with prognostic value and biological relevance.

    Science.gov (United States)

    Heidema, A Geert; Thissen, Uwe; Boer, Jolanda M A; Bouwman, Freek G; Feskens, Edith J M; Mariman, Edwin C M

    2009-06-01

    In this study, we applied the multivariate statistical tool Partial Least Squares (PLS) to analyze the relative importance of 83 plasma proteins in relation to coronary heart disease (CHD) mortality and the intermediate end points body mass index, HDL-cholesterol and total cholesterol. From a Dutch monitoring project for cardiovascular disease risk factors, men who died of CHD between initial participation (1987-1991) and end of follow-up (January 1, 2000) (N = 44) and matched controls (N = 44) were selected. Baseline plasma concentrations of proteins were measured by a multiplex immunoassay. With the use of PLS, we identified 15 proteins with prognostic value for CHD mortality and sets of proteins associated with the intermediate end points. Subsequently, sets of proteins and intermediate end points were analyzed together by Principal Components Analysis, indicating that proteins involved in inflammation explained most of the variance, followed by proteins involved in metabolism and proteins associated with total-C. This study is one of the first in which the association of a large number of plasma proteins with CHD mortality and intermediate end points is investigated by applying multivariate statistics, providing insight in the relationships among proteins, intermediate end points and CHD mortality, and a set of proteins with prognostic value.

  18. Multivariate Analysis of MODerate Resolution Imaging Spectroradiometer (MODIS Aerosol Retrievals and the Statistical Hurricane Intensity Prediction Scheme (SHIPS Parameters for Atlantic Hurricanes

    Directory of Open Access Journals (Sweden)

    Mohammed M. Kamal

    2012-09-01

    Full Text Available MODerate Resolution Imaging Spectroradiometer (MODIS aerosol retrievals over the North Atlantic spanning seven hurricane seasons are combined with the Statistical Hurricane Intensity Prediction Scheme (SHIPS parameters. The difference between the current and future intensity changes were selected as response variables. For 24 major hurricanes (category 3, 4 and 5 between 2003 and 2009, eight lead time response variables were determined to be between 6 and 48 h. By combining MODIS and SHIPS data, 56 variables were compiled and selected as predictors for this study. Variable reduction from 56 to 31 was performed in two steps; the first step was via correlation coefficients (cc followed by Principal Component Analysis (PCA extraction techniques. The PCA reduced 31 variables to 20. Five categories were established based on the PCA group variables exhibiting similar physical phenomena. Average aerosol retrievals from MODIS Level 2 data in the vicinity of UTC 1,200 and 1,800 h were mapped to the SHIPS parameters to perform Multiple Linear Regression (MLR between each response variable against six sets of predictors of 31, 30, 28, 27, 23 and 20 variables. The deviation among the predictors Root Mean Square Error (RMSE varied between 0.01 through 0.05 and, therefore, implied that reducing the number of variables did not change the core physical information. Even when the parameters are reduced from 56 to 20, the correlation values exhibit a stronger relationship between the response and predictors. Therefore, the same phenomena can be explained by the reduction of variables.

  19. A multivariate rank test for comparing mass size distributions

    KAUST Repository

    Lombard, F.; Potgieter, C. J.

    2012-01-01

    Particle size analyses of a raw material are commonplace in the mineral processing industry. Knowledge of particle size distributions is crucial in planning milling operations to enable an optimum degree of liberation of valuable mineral phases

  20. A Fast Algorithm for Generating Permutation Distribution of Ranks in ...

    African Journals Online (AJOL)

    ... function of the distribution of the ranks. This further gives insight into the permutation distribution of a rank statistics. The algorithm is implemented with the aid of the computer algebra system Mathematica. Key words: Combinatorics, generating function, permutation distribution, rank statistics, partitions, computer algebra.

  1. A tilting approach to ranking influence

    KAUST Repository

    Genton, Marc G.

    2014-12-01

    We suggest a new approach, which is applicable for general statistics computed from random samples of univariate or vector-valued or functional data, to assessing the influence that individual data have on the value of a statistic, and to ranking the data in terms of that influence. Our method is based on, first, perturbing the value of the statistic by ‘tilting’, or reweighting, each data value, where the total amount of tilt is constrained to be the least possible, subject to achieving a given small perturbation of the statistic, and, then, taking the ranking of the influence of data values to be that which corresponds to ranking the changes in data weights. It is shown, both theoretically and numerically, that this ranking does not depend on the size of the perturbation, provided that the perturbation is sufficiently small. That simple result leads directly to an elegant geometric interpretation of the ranks; they are the ranks of the lengths of projections of the weights onto a ‘line’ determined by the first empirical principal component function in a generalized measure of covariance. To illustrate the generality of the method we introduce and explore it in the case of functional data, where (for example) it leads to generalized boxplots. The method has the advantage of providing an interpretable ranking that depends on the statistic under consideration. For example, the ranking of data, in terms of their influence on the value of a statistic, is different for a measure of location and for a measure of scale. This is as it should be; a ranking of data in terms of their influence should depend on the manner in which the data are used. Additionally, the ranking recognizes, rather than ignores, sign, and in particular can identify left- and right-hand ‘tails’ of the distribution of a random function or vector.

  2. Adaptive distributional extensions to DFR ranking

    DEFF Research Database (Denmark)

    Petersen, Casper; Simonsen, Jakob Grue; Järvelin, Kalervo

    2016-01-01

    -fitting distribution. We call this model Adaptive Distributional Ranking (ADR) because it adapts the ranking to the statistics of the specific dataset being processed each time. Experiments on TREC data show ADR to outperform DFR models (and their extensions) and be comparable in performance to a query likelihood...

  3. Ranking Operations Management conferences

    NARCIS (Netherlands)

    Steenhuis, H.J.; de Bruijn, E.J.; Gupta, Sushil; Laptaned, U

    2007-01-01

    Several publications have appeared in the field of Operations Management which rank Operations Management related journals. Several ranking systems exist for journals based on , for example, perceived relevance and quality, citation, and author affiliation. Many academics also publish at conferences

  4. Ranking Theory and Conditional Reasoning.

    Science.gov (United States)

    Skovgaard-Olsen, Niels

    2016-05-01

    Ranking theory is a formal epistemology that has been developed in over 600 pages in Spohn's recent book The Laws of Belief, which aims to provide a normative account of the dynamics of beliefs that presents an alternative to current probabilistic approaches. It has long been received in the AI community, but it has not yet found application in experimental psychology. The purpose of this paper is to derive clear, quantitative predictions by exploiting a parallel between ranking theory and a statistical model called logistic regression. This approach is illustrated by the development of a model for the conditional inference task using Spohn's (2013) ranking theoretic approach to conditionals. Copyright © 2015 Cognitive Science Society, Inc.

  5. Multivariate Regression Analysis and Slaughter Livestock,

    Science.gov (United States)

    AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

  6. Introduction to multivariate discrimination

    Science.gov (United States)

    Kégl, Balázs

    2013-07-01

    Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either

  7. Introduction to multivariate discrimination

    International Nuclear Information System (INIS)

    Kegl, B.

    2013-01-01

    Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyper-parameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either

  8. Data base for the analysis of compositional characteristics of coal seams and macerals. Final report - Part 10. Variability in the inorganic content of United States' coals: a multivariate statistical study

    Energy Technology Data Exchange (ETDEWEB)

    Glick, D.C.; Davis, A.

    1984-07-01

    The multivariate statistical techniques of correlation coefficients, factor analysis, and cluster analysis, implemented by computer programs, can be used to process a large data set and produce a summary of relationships between variables and between samples. These techniques were used to find relationships for data on the inorganic constituents of US coals. Three hundred thirty-five whole-seam channel samples from six US coal provinces were analyzed for inorganic variables. After consideration of the attributes of data expressed on ash basis and whole-coal basis, it was decided to perform complete statistical analyses on both data sets. Thirty variables expressed on whole-coal basis and twenty-six variables expressed on ash basis were used. For each inorganic variable, a frequency distribution histogram and a set of summary statistics was produced. These were subdivided to reveal the manner in which concentrations of inorganic constituents vary between coal provinces and between coal regions. Data collected on 124 samples from three stratigraphic groups (Pottsville, Monongahela, Allegheny) in the Appalachian region were studied using analysis of variance to determine degree of variability between stratigraphic levels. Most variables showed differences in mean values between the three groups. 193 references, 71 figures, 54 tables.

  9. Multivariate strategies in functional magnetic resonance imaging

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    2007-01-01

    We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a `mind reading' predictive multivariate fMRI model....

  10. Sparse structure regularized ranking

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-04-17

    Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.

  11. Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

    Science.gov (United States)

    Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

    2015-03-01

    Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

  12. Exact null distributions of quadratic distribution-free statistics for two-way classification

    NARCIS (Netherlands)

    Wiel, van de M.A.

    2004-01-01

    Abstract We present new techniques for computing exact distributions of `Friedman-type¿ statistics. Representing the null distribution by a generating function allows for the use of general, not necessarily integer-valued rank scores. Moreover, we use symmetry properties of the multivariate

  13. On Rank Driven Dynamical Systems

    Science.gov (United States)

    Veerman, J. J. P.; Prieto, F. J.

    2014-08-01

    We investigate a class of models related to the Bak-Sneppen (BS) model, initially proposed to study evolution. The BS model is extremely simple and yet captures some forms of "complex behavior" such as self-organized criticality that is often observed in physical and biological systems. In this model, random fitnesses in are associated to agents located at the vertices of a graph . Their fitnesses are ranked from worst (0) to best (1). At every time-step the agent with the worst fitness and some others with a priori given rank probabilities are replaced by new agents with random fitnesses. We consider two cases: The exogenous case where the new fitnesses are taken from an a priori fixed distribution, and the endogenous case where the new fitnesses are taken from the current distribution as it evolves. We approximate the dynamics by making a simplifying independence assumption. We use Order Statistics and Dynamical Systems to define a rank-driven dynamical system that approximates the evolution of the distribution of the fitnesses in these rank-driven models, as well as in the BS model. For this simplified model we can find the limiting marginal distribution as a function of the initial conditions. Agreement with experimental results of the BS model is excellent.

  14. How to Rank Journals.

    Science.gov (United States)

    Bradshaw, Corey J A; Brook, Barry W

    2016-01-01

    There are now many methods available to assess the relative citation performance of peer-reviewed journals. Regardless of their individual faults and advantages, citation-based metrics are used by researchers to maximize the citation potential of their articles, and by employers to rank academic track records. The absolute value of any particular index is arguably meaningless unless compared to other journals, and different metrics result in divergent rankings. To provide a simple yet more objective way to rank journals within and among disciplines, we developed a κ-resampled composite journal rank incorporating five popular citation indices: Impact Factor, Immediacy Index, Source-Normalized Impact Per Paper, SCImago Journal Rank and Google 5-year h-index; this approach provides an index of relative rank uncertainty. We applied the approach to six sample sets of scientific journals from Ecology (n = 100 journals), Medicine (n = 100), Multidisciplinary (n = 50); Ecology + Multidisciplinary (n = 25), Obstetrics & Gynaecology (n = 25) and Marine Biology & Fisheries (n = 25). We then cross-compared the κ-resampled ranking for the Ecology + Multidisciplinary journal set to the results of a survey of 188 publishing ecologists who were asked to rank the same journals, and found a 0.68-0.84 Spearman's ρ correlation between the two rankings datasets. Our composite index approach therefore approximates relative journal reputation, at least for that discipline. Agglomerative and divisive clustering and multi-dimensional scaling techniques applied to the Ecology + Multidisciplinary journal set identified specific clusters of similarly ranked journals, with only Nature & Science separating out from the others. When comparing a selection of journals within or among disciplines, we recommend collecting multiple citation-based metrics for a sample of relevant and realistic journals to calculate the composite rankings and their relative uncertainty windows.

  15. RankExplorer: Visualization of Ranking Changes in Large Time Series Data.

    Science.gov (United States)

    Shi, Conglei; Cui, Weiwei; Liu, Shixia; Xu, Panpan; Chen, Wei; Qu, Huamin

    2012-12-01

    For many applications involving time series data, people are often interested in the changes of item values over time as well as their ranking changes. For example, people search many words via search engines like Google and Bing every day. Analysts are interested in both the absolute searching number for each word as well as their relative rankings. Both sets of statistics may change over time. For very large time series data with thousands of items, how to visually present ranking changes is an interesting challenge. In this paper, we propose RankExplorer, a novel visualization method based on ThemeRiver to reveal the ranking changes. Our method consists of four major components: 1) a segmentation method which partitions a large set of time series curves into a manageable number of ranking categories; 2) an extended ThemeRiver view with embedded color bars and changing glyphs to show the evolution of aggregation values related to each ranking category over time as well as the content changes in each ranking category; 3) a trend curve to show the degree of ranking changes over time; 4) rich user interactions to support interactive exploration of ranking changes. We have applied our method to some real time series data and the case studies demonstrate that our method can reveal the underlying patterns related to ranking changes which might otherwise be obscured in traditional visualizations.

  16. Paired comparisons analysis: an axiomatic approach to ranking methods

    NARCIS (Netherlands)

    Gonzalez-Diaz, J.; Hendrickx, Ruud; Lohmann, E.R.M.A.

    2014-01-01

    In this paper we present an axiomatic analysis of several ranking methods for general tournaments. We find that the ranking method obtained by applying maximum likelihood to the (Zermelo-)Bradley-Terry model, the most common method in statistics and psychology, is one of the ranking methods that

  17. On Page Rank

    NARCIS (Netherlands)

    Hoede, C.

    In this paper the concept of page rank for the world wide web is discussed. The possibility of describing the distribution of page rank by an exponential law is considered. It is shown that the concept is essentially equal to that of status score, a centrality measure discussed already in 1953 by

  18. On Rank and Nullity

    Science.gov (United States)

    Dobbs, David E.

    2012-01-01

    This note explains how Emil Artin's proof that row rank equals column rank for a matrix with entries in a field leads naturally to the formula for the nullity of a matrix and also to an algorithm for solving any system of linear equations in any number of variables. This material could be used in any course on matrix theory or linear algebra.

  19. Hitting the Rankings Jackpot

    Science.gov (United States)

    Chapman, David W.

    2008-01-01

    Recently, Samford University was ranked 27th in the nation in a report released by "Forbes" magazine. In this article, the author relates how the people working at Samford University were surprised at its ranking. Although Samford is the largest privately institution in Alabama, its distinguished academic achievements aren't even…

  20. A tilting approach to ranking influence

    KAUST Repository

    Genton, Marc G.; Hall, Peter

    2014-01-01

    We suggest a new approach, which is applicable for general statistics computed from random samples of univariate or vector-valued or functional data, to assessing the influence that individual data have on the value of a statistic, and to ranking

  1. Tile Low Rank Cholesky Factorization for Climate/Weather Modeling Applications on Manycore Architectures

    KAUST Repository

    Akbudak, Kadir; Ltaief, Hatem; Mikhalev, Aleksandr; Keyes, David E.

    2017-01-01

    Covariance matrices are ubiquitous in computational science and engineering. In particular, large covariance matrices arise from multivariate spatial data sets, for instance, in climate/weather modeling applications to improve prediction using statistical methods and spatial data. One of the most time-consuming computational steps consists in calculating the Cholesky factorization of the symmetric, positive-definite covariance matrix problem. The structure of such covariance matrices is also often data-sparse, in other words, effectively of low rank, though formally dense. While not typically globally of low rank, covariance matrices in which correlation decays with distance are nearly always hierarchically of low rank. While symmetry and positive definiteness should be, and nearly always are, exploited for performance purposes, exploiting low rank character in this context is very recent, and will be a key to solving these challenging problems at large-scale dimensions. The authors design a new and flexible tile row rank Cholesky factorization and propose a high performance implementation using OpenMP task-based programming model on various leading-edge manycore architectures. Performance comparisons and memory footprint saving on up to 200K×200K covariance matrix size show a gain of more than an order of magnitude for both metrics, against state-of-the-art open-source and vendor optimized numerical libraries, while preserving the numerical accuracy fidelity of the original model. This research represents an important milestone in enabling large-scale simulations for covariance-based scientific applications.

  2. Tile Low Rank Cholesky Factorization for Climate/Weather Modeling Applications on Manycore Architectures

    KAUST Repository

    Akbudak, Kadir

    2017-05-11

    Covariance matrices are ubiquitous in computational science and engineering. In particular, large covariance matrices arise from multivariate spatial data sets, for instance, in climate/weather modeling applications to improve prediction using statistical methods and spatial data. One of the most time-consuming computational steps consists in calculating the Cholesky factorization of the symmetric, positive-definite covariance matrix problem. The structure of such covariance matrices is also often data-sparse, in other words, effectively of low rank, though formally dense. While not typically globally of low rank, covariance matrices in which correlation decays with distance are nearly always hierarchically of low rank. While symmetry and positive definiteness should be, and nearly always are, exploited for performance purposes, exploiting low rank character in this context is very recent, and will be a key to solving these challenging problems at large-scale dimensions. The authors design a new and flexible tile row rank Cholesky factorization and propose a high performance implementation using OpenMP task-based programming model on various leading-edge manycore architectures. Performance comparisons and memory footprint saving on up to 200K×200K covariance matrix size show a gain of more than an order of magnitude for both metrics, against state-of-the-art open-source and vendor optimized numerical libraries, while preserving the numerical accuracy fidelity of the original model. This research represents an important milestone in enabling large-scale simulations for covariance-based scientific applications.

  3. Profiling and analysis of multiple constituents in Baizhu Shaoyao San before and after processing by stir-frying using UHPLC/Q-TOF-MS/MS coupled with multivariate statistical analysis.

    Science.gov (United States)

    Xu, Yangyang; Cai, Hao; Cao, Gang; Duan, Yu; Pei, Ke; Tu, Sicong; Zhou, Jia; Xie, Li; Sun, Dongdong; Zhao, Jiayu; Liu, Jing; Wang, Xiaoqi; Shen, Lin

    2018-04-15

    Baizhu Shaoyao San (BSS) is a famous traditional Chinese medicinal formula widely used for the treatment of painful diarrhea, intestinal inflammation, and diarrhea-predominant irritable bowel syndrome. According to clinical medication, three medicinal herbs (Atractylodis Macrocephalae Rhizoma, Paeoniae Radix Alba, and Citri Reticulatae Pericarpium) included in BSS must be processed using some specific methods of stir-frying. On the basis of the classical theories of traditional Chinese medicine, the therapeutic effects of BSS would be significantly enhanced after processing. Generally, the changes of curative effects mainly result from the variations of inside chemical basis caused by the processing procedure. To find out the corresponding changes of chemical compositions in BSS after processing and to elucidate the material basis of the changed curative effects, an optimized ultra-high-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry in positive and negative ion modes coupled with multivariate statistical analyses were developed. As a result, a total of 186 compounds were ultimately identified in crude and processed BSS, in which 62 marker compounds with significant differences between crude and processed BSS were found by principal component analysis and t-test. Compared with crude BSS, the contents of 23 compounds were remarkably decreased and the contents of 39 compounds showed notable increase in processed BSS. The transformation mechanisms of some changed compounds were appropriately inferred from the results. Furthermore, compounds with extremely significant differences might strengthen the effects of the whole herbal formula. Copyright © 2018 Elsevier B.V. All rights reserved.

  4. Recurrent fuzzy ranking methods

    Science.gov (United States)

    Hajjari, Tayebeh

    2012-11-01

    With the increasing development of fuzzy set theory in various scientific fields and the need to compare fuzzy numbers in different areas. Therefore, Ranking of fuzzy numbers plays a very important role in linguistic decision-making, engineering, business and some other fuzzy application systems. Several strategies have been proposed for ranking of fuzzy numbers. Each of these techniques has been shown to produce non-intuitive results in certain case. In this paper, we reviewed some recent ranking methods, which will be useful for the researchers who are interested in this area.

  5. Hierarchical partial order ranking

    International Nuclear Information System (INIS)

    Carlsen, Lars

    2008-01-01

    Assessing the potential impact on environmental and human health from the production and use of chemicals or from polluted sites involves a multi-criteria evaluation scheme. A priori several parameters are to address, e.g., production tonnage, specific release scenarios, geographical and site-specific factors in addition to various substance dependent parameters. Further socio-economic factors may be taken into consideration. The number of parameters to be included may well appear to be prohibitive for developing a sensible model. The study introduces hierarchical partial order ranking (HPOR) that remedies this problem. By HPOR the original parameters are initially grouped based on their mutual connection and a set of meta-descriptors is derived representing the ranking corresponding to the single groups of descriptors, respectively. A second partial order ranking is carried out based on the meta-descriptors, the final ranking being disclosed though average ranks. An illustrative example on the prioritisation of polluted sites is given. - Hierarchical partial order ranking of polluted sites has been developed for prioritization based on a large number of parameters

  6. Evaluation of the osteoclastogenic process associated with RANK / RANK-L / OPG in odontogenic myxomas

    Science.gov (United States)

    González-Galván, María del Carmen; Mosqueda-Taylor, Adalberto; Bologna-Molina, Ronell; Setien-Olarra, Amaia; Marichalar-Mendia, Xabier; Aguirre-Urizar, José-Manuel

    2018-01-01

    Background Odontogenic myxoma (OM) is a benign intraosseous neoplasm that exhibits local aggressiveness and high recurrence rates. Osteoclastogenesis is an important phenomenon in the tumor growth of maxillary neoplasms. RANK (Receptor Activator of Nuclear Factor κappa B) is the signaling receptor of RANK-L (Receptor activator of nuclear factor kappa-Β ligand) that activates the osteoclasts. OPG (osteoprotegerin) is a decoy receptor for RANK-L that inhibits pro-osteoclastogenesis. The RANK / RANKL / OPG system participates in the regulation of osteolytic activity under normal conditions, and its alteration has been associated with greater bone destruction, and also with tumor growth. Objectives To analyze the immunohistochemical expression of OPG, RANK and RANK-L proteins in odontogenic myxomas (OMs) and their relationship with the tumor size. Material and Methods Eighteen OMs, 4 small ( 3cm) and 18 dental follicles (DF) that were included as control were studied by means of standard immunohistochemical procedure with RANK, RANKL and OPG antibodies. For the evaluation, 5 fields (40x) of representative areas of OM and DF were selected where the expression of each antibody was determined. Descriptive and comparative statistical analyses were performed with the obtained data. Results There are significant differences in the expression of RANK in OM samples as compared to DF (p = 0.022) and among the OMSs and OMLs (p = 0.032). Also a strong association is recognized in the expression of RANK-L and OPG in OM samples. Conclusions Activation of the RANK / RANK-L / OPG triad seems to be involved in the mechanisms of bone balance and destruction, as well as associated with tumor growth in odontogenic myxomas. Key words:Odontogenic myxoma, dental follicle, RANK, RANK-L, OPG, osteoclastogenesis. PMID:29680857

  7. Low Rank Approximation Algorithms, Implementation, Applications

    CERN Document Server

    Markovsky, Ivan

    2012-01-01

    Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequently in many different fields. Low Rank Approximation: Algorithms, Implementation, Applications is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. A major part of the text is devoted to application of the theory. Applications described include: system and control theory: approximate realization, model reduction, output error, and errors-in-variables identification; signal processing: harmonic retrieval, sum-of-damped exponentials, finite impulse response modeling, and array processing; machine learning: multidimensional scaling and recommender system; computer vision: algebraic curve fitting and fundamental matrix estimation; bioinformatics for microarray data analysis; chemometrics for multivariate calibration; ...

  8. Emulating facial biomechanics using multivariate partial least squares surrogate models.

    Science.gov (United States)

    Wu, Tim; Martens, Harald; Hunter, Peter; Mithraratne, Kumar

    2014-11-01

    A detailed biomechanical model of the human face driven by a network of muscles is a useful tool in relating the muscle activities to facial deformations. However, lengthy computational times often hinder its applications in practical settings. The objective of this study is to replace precise but computationally demanding biomechanical model by a much faster multivariate meta-model (surrogate model), such that a significant speedup (to real-time interactive speed) can be achieved. Using a multilevel fractional factorial design, the parameter space of the biomechanical system was probed from a set of sample points chosen to satisfy maximal rank optimality and volume filling. The input-output relationship at these sampled points was then statistically emulated using linear and nonlinear, cross-validated, partial least squares regression models. It was demonstrated that these surrogate models can mimic facial biomechanics efficiently and reliably in real-time. Copyright © 2014 John Wiley & Sons, Ltd.

  9. Multiplex PageRank.

    Science.gov (United States)

    Halu, Arda; Mondragón, Raúl J; Panzarasa, Pietro; Bianconi, Ginestra

    2013-01-01

    Many complex systems can be described as multiplex networks in which the same nodes can interact with one another in different layers, thus forming a set of interacting and co-evolving networks. Examples of such multiplex systems are social networks where people are involved in different types of relationships and interact through various forms of communication media. The ranking of nodes in multiplex networks is one of the most pressing and challenging tasks that research on complex networks is currently facing. When pairs of nodes can be connected through multiple links and in multiple layers, the ranking of nodes should necessarily reflect the importance of nodes in one layer as well as their importance in other interdependent layers. In this paper, we draw on the idea of biased random walks to define the Multiplex PageRank centrality measure in which the effects of the interplay between networks on the centrality of nodes are directly taken into account. In particular, depending on the intensity of the interaction between layers, we define the Additive, Multiplicative, Combined, and Neutral versions of Multiplex PageRank, and show how each version reflects the extent to which the importance of a node in one layer affects the importance the node can gain in another layer. We discuss these measures and apply them to an online multiplex social network. Findings indicate that taking the multiplex nature of the network into account helps uncover the emergence of rankings of nodes that differ from the rankings obtained from one single layer. Results provide support in favor of the salience of multiplex centrality measures, like Multiplex PageRank, for assessing the prominence of nodes embedded in multiple interacting networks, and for shedding a new light on structural properties that would otherwise remain undetected if each of the interacting networks were analyzed in isolation.

  10. Multiplex PageRank.

    Directory of Open Access Journals (Sweden)

    Arda Halu

    Full Text Available Many complex systems can be described as multiplex networks in which the same nodes can interact with one another in different layers, thus forming a set of interacting and co-evolving networks. Examples of such multiplex systems are social networks where people are involved in different types of relationships and interact through various forms of communication media. The ranking of nodes in multiplex networks is one of the most pressing and challenging tasks that research on complex networks is currently facing. When pairs of nodes can be connected through multiple links and in multiple layers, the ranking of nodes should necessarily reflect the importance of nodes in one layer as well as their importance in other interdependent layers. In this paper, we draw on the idea of biased random walks to define the Multiplex PageRank centrality measure in which the effects of the interplay between networks on the centrality of nodes are directly taken into account. In particular, depending on the intensity of the interaction between layers, we define the Additive, Multiplicative, Combined, and Neutral versions of Multiplex PageRank, and show how each version reflects the extent to which the importance of a node in one layer affects the importance the node can gain in another layer. We discuss these measures and apply them to an online multiplex social network. Findings indicate that taking the multiplex nature of the network into account helps uncover the emergence of rankings of nodes that differ from the rankings obtained from one single layer. Results provide support in favor of the salience of multiplex centrality measures, like Multiplex PageRank, for assessing the prominence of nodes embedded in multiple interacting networks, and for shedding a new light on structural properties that would otherwise remain undetected if each of the interacting networks were analyzed in isolation.

  11. Application of multivariate statistical analysis and hydrochemical and isotopic investigations for evaluation of groundwater quality and its suitability for drinking and agriculture purposes: case of Oum Ali-Thelepte aquifer, central Tunisia.

    Science.gov (United States)

    Hassen, Imen; Hamzaoui-Azaza, Fadoua; Bouhlila, Rachida

    2016-03-01

    Groundwater plays a dominant role in arid regions; it is among the most available water resources in Tunisia. Located in northwestern Tunisia, Oum Ali-Thelepte is a deep Miocene sedimentary aquifer, where groundwater is the most important source of water supply. The aim of the study is to investigate the hydrochemical processes leading to mineralization and to assess water quality with respect to agriculture and drinking for a better management of groundwater resources. To achieve such objectives, water analysis was carried out on 16 groundwater samples collected during January-February 2014. Stable isotopes and 26 hydrochemical parameters were examined. The interpretation of these analytical data showed that the concentrations of major and trace elements were within the permissible level for human use. The distribution of mineral processes in this aquifer was identified using conventional classification techniques, suggesting that the water facies gradually changes from Ca-HCO3 to Mg-SO4 type and are controlled by water-rock interaction. These results were endorsed using multivariate statistical methods such as principal component analysis and cluster analysis. The sustainability of groundwater for drinking and irrigation was assessed based on the water quality index (WQI) and on Wilcox and Richards's diagrams. This aquifer has been classified as "excellent water" serving good irrigation in the area. As for the stable isotope, the measurements showed that groundwater samples lay between global meteoric water line (GMWL) and LMWL; hence, this arrangement signifies that the recharge of the Oum Ali-Thelepte aquifer is ensured by rainwater infiltration through mountains in the border of the aquifer without evaporation effects.

  12. Screening and analysis of potential anti-tumor components from the stipe of Ganoderma sinense using high-performance liquid chromatography/time-of-flight mass spectrometry with multivariate statistical tool.

    Science.gov (United States)

    Chan, Kar-Man; Yue, Grace Gar-Lee; Li, Ping; Wong, Eric Chun-Wai; Lee, Julia Kin-Ming; Kennelly, Edward J; Lau, Clara Bik-San

    2017-03-03

    According to Chinese Pharmacopoeia 2015 edition, Ganoderma (Lingzhi) is a species complex that comprise of Ganoderma lucidum and Ganoderma sinense. The bioactivity and chemical composition of G. lucidium had been studied extensively, and it was shown to possess antitumor activities in pharmacological studies. In contrast, G. sinense has not been studied in great detail. Our previous studies found that the stipe of G. sinense exhibited more potent antitumor activity than the pileus. To identify the antitumor compounds in the stipe of G. sinense, we studied its chemical components by merging the bioactivity results with liquid chromatography-mass spectrometry-based chemometrics. The stipe of G. sinense was extracted with water, followed by ethanol precipitation and liquid-liquid partition. The resulting residue was fractionated using column chromatography. The antitumor activity of these fractions were analysed using MTT assay in murine breast tumor 4T1 cells, and their chemical components were studied using the LC-QTOF-MS with multivariate statistical tools. The chemometric and MS/MS analysis correlated bioactivity with five known cytotoxic compounds, 4-hyroxyphenylacetate, 9-oxo-(10E,12E)-octadecadienoic acid, 3-phenyl-2-propenoic acid, 13-oxo-(9E,11E)-octadecadienoic acid and lingzhine C, from the stipe of G. sinense. To the best of our knowledge, 4-hyroxyphenylacetate, 3-phenyl-2-propenoic acid and lingzhine C are firstly reported to be found in G. sinense. These five compounds will be investigated for their antitumor activities in the future. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Algebraic and computational aspects of real tensor ranks

    CERN Document Server

    Sakata, Toshio; Miyazaki, Mitsuhiro

    2016-01-01

    This book provides comprehensive summaries of theoretical (algebraic) and computational aspects of tensor ranks, maximal ranks, and typical ranks, over the real number field. Although tensor ranks have been often argued in the complex number field, it should be emphasized that this book treats real tensor ranks, which have direct applications in statistics. The book provides several interesting ideas, including determinant polynomials, determinantal ideals, absolutely nonsingular tensors, absolutely full column rank tensors, and their connection to bilinear maps and Hurwitz-Radon numbers. In addition to reviews of methods to determine real tensor ranks in details, global theories such as the Jacobian method are also reviewed in details. The book includes as well an accessible and comprehensive introduction of mathematical backgrounds, with basics of positive polynomials and calculations by using the Groebner basis. Furthermore, this book provides insights into numerical methods of finding tensor ranks through...

  14. Groundwater contaminant plume ranking

    International Nuclear Information System (INIS)

    1988-08-01

    Containment plumes at Uranium Mill Tailings Remedial Action (UMTRA) Project sites were ranked to assist in Subpart B (i.e., restoration requirements of 40 CFR Part 192) compliance strategies for each site, to prioritize aquifer restoration, and to budget future requests and allocations. The rankings roughly estimate hazards to the environment and human health, and thus assist in determining for which sites cleanup, if appropriate, will provide the greatest benefits for funds available. The rankings are based on the scores that were obtained using the US Department of Energy's (DOE) Modified Hazard Ranking System (MHRS). The MHRS and HRS consider and score three hazard modes for a site: migration, fire and explosion, and direct contact. The migration hazard mode score reflects the potential for harm to humans or the environment from migration of a hazardous substance off a site by groundwater, surface water, and air; it is a composite of separate scores for each of these routes. For ranking the containment plumes at UMTRA Project sites, it was assumed that each site had been remediated in compliance with the EPA standards and that relict contaminant plumes were present. Therefore, only the groundwater route was scored, and the surface water and air routes were not considered. Section 2.0 of this document describes the assumptions and procedures used to score the groundwater route, and Section 3.0 provides the resulting scores for each site. 40 tabs

  15. Co-integration Rank Testing under Conditional Heteroskedasticity

    DEFF Research Database (Denmark)

    Cavaliere, Guiseppe; Rahbæk, Anders; Taylor, A.M. Robert

    null distributions of the rank statistics coincide with those derived by previous authors who assume either i.i.d. or (strict and covariance) stationary martingale difference innovations. We then propose wild bootstrap implementations of the co-integrating rank tests and demonstrate that the associated...... bootstrap rank statistics replicate the first-order asymptotic null distributions of the rank statistics. We show the same is also true of the corresponding rank tests based on the i.i.d. bootstrap of Swensen (2006). The wild bootstrap, however, has the important property that, unlike the i.i.d. bootstrap......, it preserves in the re-sampled data the pattern of heteroskedasticity present in the original shocks. Consistent with this, numerical evidence sug- gests that, relative to tests based on the asymptotic critical values or the i.i.d. bootstrap, the wild bootstrap rank tests perform very well in small samples un...

  16. Simplicial band depth for multivariate functional data

    KAUST Repository

    Ló pez-Pintado, Sara; Sun, Ying; Lin, Juan K.; Genton, Marc G.

    2014-01-01

    sample of curves. Based on these depths, a sample of multivariate curves can be ordered from the center outward and order statistics can be defined. Properties of the proposed depths, such as invariance and consistency, can be established. A simulation

  17. An Introduction to Applied Multivariate Analysis

    CERN Document Server

    Raykov, Tenko

    2008-01-01

    Focuses on the core multivariate statistics topics which are of fundamental relevance for its understanding. This book emphasis on the topics that are critical to those in the behavioral, social, and educational sciences.

  18. Ranking economic history journals

    DEFF Research Database (Denmark)

    Di Vaio, Gianfranco; Weisdorf, Jacob Louis

    2010-01-01

    This study ranks-for the first time-12 international academic journals that have economic history as their main topic. The ranking is based on data collected for the year 2007. Journals are ranked using standard citation analysis where we adjust for age, size and self-citation of journals. We also...... compare the leading economic history journals with the leading journals in economics in order to measure the influence on economics of economic history, and vice versa. With a few exceptions, our results confirm the general idea about what economic history journals are the most influential for economic...... history, and that, although economic history is quite independent from economics as a whole, knowledge exchange between the two fields is indeed going on....

  19. Ranking Economic History Journals

    DEFF Research Database (Denmark)

    Di Vaio, Gianfranco; Weisdorf, Jacob Louis

    This study ranks - for the first time - 12 international academic journals that have economic history as their main topic. The ranking is based on data collected for the year 2007. Journals are ranked using standard citation analysis where we adjust for age, size and self-citation of journals. We...... also compare the leading economic history journals with the leading journals in economics in order to measure the influence on economics of economic history, and vice versa. With a few exceptions, our results confirm the general idea about what economic history journals are the most influential...... for economic history, and that, although economic history is quite independent from economics as a whole, knowledge exchange between the two fields is indeed going on....

  20. Dynamic Matrix Rank

    DEFF Research Database (Denmark)

    Frandsen, Gudmund Skovbjerg; Frandsen, Peter Frands

    2009-01-01

    We consider maintaining information about the rank of a matrix under changes of the entries. For n×n matrices, we show an upper bound of O(n1.575) arithmetic operations and a lower bound of Ω(n) arithmetic operations per element change. The upper bound is valid when changing up to O(n0.575) entries...... in a single column of the matrix. We also give an algorithm that maintains the rank using O(n2) arithmetic operations per rank one update. These bounds appear to be the first nontrivial bounds for the problem. The upper bounds are valid for arbitrary fields, whereas the lower bound is valid for algebraically...... closed fields. The upper bound for element updates uses fast rectangular matrix multiplication, and the lower bound involves further development of an earlier technique for proving lower bounds for dynamic computation of rational functions....

  1. Diversifying customer review rankings.

    Science.gov (United States)

    Krestel, Ralf; Dokoohaki, Nima

    2015-06-01

    E-commerce Web sites owe much of their popularity to consumer reviews accompanying product descriptions. On-line customers spend hours and hours going through heaps of textual reviews to decide which products to buy. At the same time, each popular product has thousands of user-generated reviews, making it impossible for a buyer to read everything. Current approaches to display reviews to users or recommend an individual review for a product are based on the recency or helpfulness of each review. In this paper, we present a framework to rank product reviews by optimizing the coverage of the ranking with respect to sentiment or aspects, or by summarizing all reviews with the top-K reviews in the ranking. To accomplish this, we make use of the assigned star rating for a product as an indicator for a review's sentiment polarity and compare bag-of-words (language model) with topic models (latent Dirichlet allocation) as a mean to represent aspects. Our evaluation on manually annotated review data from a commercial review Web site demonstrates the effectiveness of our approach, outperforming plain recency ranking by 30% and obtaining best results by combining language and topic model representations. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. College Rankings. ERIC Digest.

    Science.gov (United States)

    Holub, Tamara

    The popularity of college ranking surveys published by "U.S. News and World Report" and other magazines is indisputable, but the methodologies used to measure the quality of higher education institutions have come under fire by scholars and college officials. Criticisms have focused on methodological flaws, such as failure to consider…

  3. OutRank

    DEFF Research Database (Denmark)

    Müller, Emmanuel; Assent, Ira; Steinhausen, Uwe

    2008-01-01

    Outlier detection is an important data mining task for consistency checks, fraud detection, etc. Binary decision making on whether or not an object is an outlier is not appropriate in many applications and moreover hard to parametrize. Thus, recently, methods for outlier ranking have been proposed...

  4. An R package for analyzing and modeling ranking data.

    Science.gov (United States)

    Lee, Paul H; Yu, Philip L H

    2013-05-14

    In medical informatics, psychology, market research and many other fields, researchers often need to analyze and model ranking data. However, there is no statistical software that provides tools for the comprehensive analysis of ranking data. Here, we present pmr, an R package for analyzing and modeling ranking data with a bundle of tools. The pmr package enables descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce model, distance-based model, and rank-ordered logit model), and the visualization of ranking data with multidimensional preference analysis. Examples of the use of package pmr are given using a real ranking dataset from medical informatics, in which 566 Hong Kong physicians ranked the top five incentives (1: competitive pressures; 2: increased savings; 3: government regulation; 4: improved efficiency; 5: improved quality care; 6: patient demand; 7: financial incentives) to the computerization of clinical practice. The mean rank showed that item 4 is the most preferred item and item 3 is the least preferred item, and significance difference was found between physicians' preferences with respect to their monthly income. A multidimensional preference analysis identified two dimensions that explain 42% of the total variance. The first can be interpreted as the overall preference of the seven items (labeled as "internal/external"), and the second dimension can be interpreted as their overall variance of (labeled as "push/pull factors"). Various statistical models were fitted, and the best were found to be weighted distance-based models with Spearman's footrule distance. In this paper, we presented the R package pmr, the first package for analyzing and modeling ranking data. The package provides insight to users through descriptive statistics of ranking data. Users can also visualize ranking data by applying a thought

  5. Perils of parsimony: properties of reduced-rank estimates of genetic covariance matrices.

    Science.gov (United States)

    Meyer, Karin; Kirkpatrick, Mark

    2008-10-01

    Eigenvalues and eigenvectors of covariance matrices are important statistics for multivariate problems in many applications, including quantitative genetics. Estimates of these quantities are subject to different types of bias. This article reviews and extends the existing theory on these biases, considering a balanced one-way classification and restricted maximum-likelihood estimation. Biases are due to the spread of sample roots and arise from ignoring selected principal components when imposing constraints on the parameter space, to ensure positive semidefinite estimates or to estimate covariance matrices of chosen, reduced rank. In addition, it is shown that reduced-rank estimators that consider only the leading eigenvalues and -vectors of the "between-group" covariance matrix may be biased due to selecting the wrong subset of principal components. In a genetic context, with groups representing families, this bias is inverse proportional to the degree of genetic relationship among family members, but is independent of sample size. Theoretical results are supplemented by a simulation study, demonstrating close agreement between predicted and observed bias for large samples. It is emphasized that the rank of the genetic covariance matrix should be chosen sufficiently large to accommodate all important genetic principal components, even though, paradoxically, this may require including a number of components with negligible eigenvalues. A strategy for rank selection in practical analyses is outlined.

  6. Ranking Workplace Competencies: Student and Graduate Perceptions.

    Science.gov (United States)

    Rainsbury, Elizabeth; Hodges, Dave; Burchell, Noel; Lay, Mark

    2002-01-01

    New Zealand business students and graduates made similar rankings of the five most important workplace competencies: computer literacy, customer service orientation, teamwork and cooperation, self-confidence, and willingness to learn. Graduates placed greater importance on most of the 24 competencies, resulting in a statistically significant…

  7. Improving Ranking Using Quantum Probability

    OpenAIRE

    Melucci, Massimo

    2011-01-01

    The paper shows that ranking information units by quantum probability differs from ranking them by classical probability provided the same data used for parameter estimation. As probability of detection (also known as recall or power) and probability of false alarm (also known as fallout or size) measure the quality of ranking, we point out and show that ranking by quantum probability yields higher probability of detection than ranking by classical probability provided a given probability of ...

  8. 1991 Acceptance priority ranking

    International Nuclear Information System (INIS)

    1991-12-01

    The Standard Contract for Disposal of Spent Nuclear Fuel and/or High- Level Radioactive Waste (10 CFR Part 961) that the Department of Energy (DOE) has executed with the owners and generators of civilian spent nuclear fuel requires annual publication of the Acceptance Priority Ranking (APR). The 1991 APR details the order in which DOE will allocate Federal waste acceptance capacity. As required by the Standard Contract, the ranking is based on the age of permanently discharged spent nuclear fuel (SNF), with the owners of the oldest SNF, on an industry-wide basis, given the highest priority. the 1991 APR will be the basis for the annual allocation of waste acceptance capacity to the Purchasers in the 1991 Annual Capacity Report (ACR), to be issued later this year. This document is based on SNF discharges as of December 31, 1990, and reflects Purchaser comments and corrections, as appropriate, to the draft APR issued on May 15, 1991

  9. Asympotic efficiency of signed - rank symmetry tests under skew alternatives.

    OpenAIRE

    Alessandra Durio; Yakov Nikitin

    2002-01-01

    The efficiency of some known tests for symmetry such as the sign test, the Wilcoxon signed-rank test or more general linear signed rank tests was studied mainly under the classical alternatives of location. However it is interesting to compare the efficiencies of these tests under asymmetric alternatives like the so-called skew alternative proposed in Azzalini (1985). We find and compare local Bahadur efficiencies of linear signed-rank statistics for skew alternatives and discuss also the con...

  10. Selection and grouping of indicators of water quality using Multivariate StatisticsSeleção e agrupamento de indicadores da qualidade de águas utilizando Estatística Multivariada

    Directory of Open Access Journals (Sweden)

    Ana Paula Almeida Bertossi

    2013-10-01

    Full Text Available Multivariate statistics techniques (Principal Component Analysis and Cluster Analysis were employed to select the most important parameters that explain water quality variability at a rural watershed in the state of Espírito Santo (Brazil. In addition to group the waters studied for the similarity of features selected to verify the effect of type of soil cover (agriculture, livestock, forest and urban, water resource (surface and underground and sampling period (rainy and dry seasons. Nineteen physico-chemical parameters of water quality were analyzed: pH, electrical conductivity, total solids, total dissolved solids, total suspended solids, turbidity, biochemical oxygen demand (BOD, ammoniacal nitrogen, nitrate, nitrite, total phosphorous, Ca, Mg, Fe, Na, K, Zn, Cu and total coliform. Application of Principal Component Analysis reduced the 19 parameters to three components that explained 87.53% of the total variance of data set. Water quality parameters that best explained variability of data were: electrical conductivity, total solids, total dissolved solids, turbidity, BOD, nitrate, Ca, Mg, and Na. Application of Cluster Analysis showed four different groups of water quality that differed in concentration of physicochemical characteristics and the type of water resource study, since the collection periods and the type of soil cover did not influence the segregation of groups formed. No presente trabalho empregaram-se técnicas de Estatística Multivariada (Análise de Componentes Principais e Análise de Agrupamento Hierárquico com o objetivo de selecionar as características físico-químicas mais importantes para explicar a variabilidade da qualidade das águas de uma sub-bacia hidrográfica rural no Sul do Estado do Espírito Santo, além de agrupar as águas estudadas quanto à similaridade das características selecionadas para verificar o efeito do tipo de cobertura do solo (agrícola, pecuário, florestal e urbano, de recurso h

  11. ATTITUDES OF FOOTBALL PLAYERS OF DIFFERENT RANKS TOWARDS TRAINING EXERCISES OF RECOVERY

    Directory of Open Access Journals (Sweden)

    Slavko Molnar

    2009-11-01

    Full Text Available Training exercises of recovery of sportsmen include both exercises implying hig- her level of training and exercises of optimal recovery of sportsmen. In order to use training exercises of recovery best way it is necessary to know how they react to the body of a sportsman concerning their character and length. It is also necessary to take into consideration the fact that human body adapts equally both to burden and adequ- ate recovery exercises. To increase the efficiency of training exercises of recovery it is inevitable to use different exercises and methods changing their use, that is, they should be applied in a complex way considering their change depending on sport activities and structure of training process. The aim of the study is to check and analyse attitudes of fooyball players of different ranks towards training exercises of recovery. The sample of examinees consists of 120 footballers of different ranks (super league of Serbia = 43, the first league of Serbia = 40 and Serbian league = 37. The sample of variables consists of the system of 10 attitudes (claims, and each attitude (claim consists of 5 verbal ca- tegories (marked from -2 to +2. To determine multivariate and univariate significance of differences between footballers of different competition ranks multivariate analysis of variance (MANOVA and univariate analysis of variance (ANOVA have been ap- plied. Concerning the attitudes of training exercises of recovery statistically significant differences were got between the groups where 7 out of 10 attitudes were different. Con- cerning the correlation two types of attitudes were noticed. Footballers of different competition ranks do not have the same attitudes towards training exercises of recovery.

  12. Continuous multivariate exponential extension

    International Nuclear Information System (INIS)

    Block, H.W.

    1975-01-01

    The Freund-Weinman multivariate exponential extension is generalized to the case of nonidentically distributed marginal distributions. A fatal shock model is given for the resulting distribution. Results in the bivariate case and the concept of constant multivariate hazard rate lead to a continuous distribution related to the multivariate exponential distribution (MVE) of Marshall and Olkin. This distribution is shown to be a special case of the extended Freund-Weinman distribution. A generalization of the bivariate model of Proschan and Sullo leads to a distribution which contains both the extended Freund-Weinman distribution and the MVE

  13. Methods of Multivariate Analysis

    CERN Document Server

    Rencher, Alvin C

    2012-01-01

    Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere."-IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life sit

  14. Multivariate GARCH models

    DEFF Research Database (Denmark)

    Silvennoinen, Annastiina; Teräsvirta, Timo

    This article contains a review of multivariate GARCH models. Most common GARCH models are presented and their properties considered. This also includes nonparametric and semiparametric models. Existing specification and misspecification tests are discussed. Finally, there is an empirical example...

  15. Multivariate Time Series Search

    Data.gov (United States)

    National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...

  16. Ranking Baltic States Researchers

    Directory of Open Access Journals (Sweden)

    Gyula Mester

    2017-10-01

    Full Text Available In this article, using the h-index and the total number of citations, the best 10 Lithuanian, Latvian and Estonian researchers from several disciplines are ranked. The list may be formed based on the h-index and the total number of citations, given in Web of Science, Scopus, Publish or Perish Program and Google Scholar database. Data for the first 10 researchers are presented. Google Scholar is the most complete. Therefore, to define a single indicator, h-index calculated by Google Scholar may be a good and simple one. The author chooses the Google Scholar database as it is the broadest one.

  17. Fourth-rank cosmology

    International Nuclear Information System (INIS)

    Marrakchi, A.E.L.; Tapia, V.

    1992-05-01

    Some cosmological implications of the recently proposed fourth-rank theory of gravitation are studied. The model exhibits the possibility of being free from the horizon and flatness problems at the price of introducing a negative pressure. The field equations we obtain are compatible with k obs =0 and Ω obs t clas approx. 10 20 t Planck approx. 10 -23 s. When interpreted at the light of General Relativity the treatment is shown to be almost equivalent to that of the standard model of cosmology combined with the inflationary scenario. Hence, an interpretation of the negative pressure hypothesis is provided. (author). 8 refs

  18. University Rankings and Social Science

    OpenAIRE

    Marginson, S.

    2014-01-01

    University rankings widely affect the behaviours of prospective students and their families, university executive leaders, academic faculty, governments and investors in higher education. Yet the social science foundations of global rankings receive little scrutiny. Rankings that simply recycle reputation without any necessary connection to real outputs are of no common value. It is necessary that rankings be soundly based in scientific terms if a virtuous relationship between performance and...

  19. Essentials of multivariate data analysis

    CERN Document Server

    Spencer, Neil H

    2013-01-01

    ""… this text provides an overview at an introductory level of several methods in multivariate data analysis. It contains in-depth examples from one data set woven throughout the text, and a free [Excel] Add-In to perform the analyses in Excel, with step-by-step instructions provided for each technique. … could be used as a text (possibly supplemental) for courses in other fields where researchers wish to apply these methods without delving too deeply into the underlying statistics.""-The American Statistician, February 2015

  20. University Rankings and Social Science

    Science.gov (United States)

    Marginson, Simon

    2014-01-01

    University rankings widely affect the behaviours of prospective students and their families, university executive leaders, academic faculty, governments and investors in higher education. Yet the social science foundations of global rankings receive little scrutiny. Rankings that simply recycle reputation without any necessary connection to real…

  1. Multivariate meta-analysis: Potential and promise

    Science.gov (United States)

    Jackson, Dan; Riley, Richard; White, Ian R

    2011-01-01

    The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

  2. Simultaneous grouping and ranking with combination of SOM and TOPSIS for selection of preferable analytical procedure for furan determination in food.

    Science.gov (United States)

    Jędrkiewicz, Renata; Tsakovski, Stefan; Lavenu, Aurore; Namieśnik, Jacek; Tobiszewski, Marek

    2018-02-01

    Novel methodology for grouping and ranking with application of self-organizing maps and multicriteria decision analysis is presented. The dataset consists of 22 objects that are analytical procedures applied to furan determination in food samples. They are described by 10 variables, referred to their analytical performance, environmental and economic aspects. Multivariate statistics analysis allows to limit the amount of input data for ranking analysis. Assessment results show that the most beneficial procedures are based on microextraction techniques with GC-MS final determination. It is presented how the information obtained from both tools complement each other. The applicability of combination of grouping and ranking is also discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Global network centrality of university rankings

    Science.gov (United States)

    Guo, Weisi; Del Vecchio, Marco; Pogrebna, Ganna

    2017-10-01

    Universities and higher education institutions form an integral part of the national infrastructure and prestige. As academic research benefits increasingly from international exchange and cooperation, many universities have increased investment in improving and enabling their global connectivity. Yet, the relationship of university performance and its global physical connectedness has not been explored in detail. We conduct, to our knowledge, the first large-scale data-driven analysis into whether there is a correlation between university relative ranking performance and its global connectivity via the air transport network. The results show that local access to global hubs (as measured by air transport network betweenness) strongly and positively correlates with the ranking growth (statistical significance in different models ranges between 5% and 1% level). We also found that the local airport's aggregate flight paths (degree) and capacity (weighted degree) has no effect on university ranking, further showing that global connectivity distance is more important than the capacity of flight connections. We also examined the effect of local city economic development as a confounding variable and no effect was observed suggesting that access to global transportation hubs outweighs economic performance as a determinant of university ranking. The impact of this research is that we have determined the importance of the centrality of global connectivity and, hence, established initial evidence for further exploring potential connections between university ranking and regional investment policies on improving global connectivity.

  4. Fractional cointegration rank estimation

    DEFF Research Database (Denmark)

    Lasak, Katarzyna; Velasco, Carlos

    the parameters of the model under the null hypothesis of the cointegration rank r = 1, 2, ..., p-1. This step provides consistent estimates of the cointegration degree, the cointegration vectors, the speed of adjustment to the equilibrium parameters and the common trends. In the second step we carry out a sup......-likelihood ratio test of no-cointegration on the estimated p - r common trends that are not cointegrated under the null. The cointegration degree is re-estimated in the second step to allow for new cointegration relationships with different memory. We augment the error correction model in the second step...... to control for stochastic trend estimation effects from the first step. The critical values of the tests proposed depend only on the number of common trends under the null, p - r, and on the interval of the cointegration degrees b allowed, but not on the true cointegration degree b0. Hence, no additional...

  5. Multivariate Birkhoff interpolation

    CERN Document Server

    Lorentz, Rudolph A

    1992-01-01

    The subject of this book is Lagrange, Hermite and Birkhoff (lacunary Hermite) interpolation by multivariate algebraic polynomials. It unifies and extends a new algorithmic approach to this subject which was introduced and developed by G.G. Lorentz and the author. One particularly interesting feature of this algorithmic approach is that it obviates the necessity of finding a formula for the Vandermonde determinant of a multivariate interpolation in order to determine its regularity (which formulas are practically unknown anyways) by determining the regularity through simple geometric manipulations in the Euclidean space. Although interpolation is a classical problem, it is surprising how little is known about its basic properties in the multivariate case. The book therefore starts by exploring its fundamental properties and its limitations. The main part of the book is devoted to a complete and detailed elaboration of the new technique. A chapter with an extensive selection of finite elements follows as well a...

  6. Rankings, creatividad y urbanismo

    Directory of Open Access Journals (Sweden)

    JOAQUÍN SABATÉ

    2008-08-01

    Full Text Available La competencia entre ciudades constituye uno de los factores impulsores de procesos de renovación urbana y los rankings han devenido instrumentos de medida de la calidad de las ciudades. Nos detendremos en el caso de un antiguo barrio industrial hoy en vías de transformación en distrito "creativo" por medio de una intervención urbanística de gran escala. Su análisis nos descubre tres claves críticas. En primer lugar, nos obliga a plantearnos la definición de innovación urbana y cómo se integran el pasado, la identidad y la memoria en la construcción del futuro. Nos lleva a comprender que la innovación y el conocimiento no se "dan" casualmente, sino que son el fruto de una larga y compleja red en la que participan saberes, espacios, actores e instituciones diversas en naturaleza, escala y magnitud. Por último nos obliga a reflexionar sobre el valor que se le otorga a lo local en los procesos de renovación urbana.Competition among cities constitutes one ofthe main factors o furban renewal, and rankings have become instruments to indícate cities quality. Studying the transformation of an old industrial quarter into a "creative district" by the means ofa large scale urban project we highlight three main conclusions. First, itasks us to reconsider the notion ofurban innovation and hoto past, identity and memory should intégrate the future development. Second, it shows that innovation and knowledge doesn't yield per chance, but are the result ofa large and complex grid of diverse knowledges, spaces, agents and institutions. Finally itforces us to reflect about the valué attributed to the "local" in urban renewalprocesses.

  7. Statistical analysis of management data

    CERN Document Server

    Gatignon, Hubert

    2013-01-01

    This book offers a comprehensive approach to multivariate statistical analyses. It provides theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications.

  8. Multiparametric statistics

    CERN Document Server

    Serdobolskii, Vadim Ivanovich

    2007-01-01

    This monograph presents mathematical theory of statistical models described by the essentially large number of unknown parameters, comparable with sample size but can also be much larger. In this meaning, the proposed theory can be called "essentially multiparametric". It is developed on the basis of the Kolmogorov asymptotic approach in which sample size increases along with the number of unknown parameters.This theory opens a way for solution of central problems of multivariate statistics, which up until now have not been solved. Traditional statistical methods based on the idea of an infinite sampling often break down in the solution of real problems, and, dependent on data, can be inefficient, unstable and even not applicable. In this situation, practical statisticians are forced to use various heuristic methods in the hope the will find a satisfactory solution.Mathematical theory developed in this book presents a regular technique for implementing new, more efficient versions of statistical procedures. ...

  9. Ranking nodes in growing networks: When PageRank fails.

    Science.gov (United States)

    Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng

    2015-11-10

    PageRank is arguably the most popular ranking algorithm which is being applied in real systems ranging from information to biological and infrastructure networks. Despite its outstanding popularity and broad use in different areas of science, the relation between the algorithm's efficacy and properties of the network on which it acts has not yet been fully understood. We study here PageRank's performance on a network model supported by real data, and show that realistic temporal effects make PageRank fail in individuating the most valuable nodes for a broad range of model parameters. Results on real data are in qualitative agreement with our model-based findings. This failure of PageRank reveals that the static approach to information filtering is inappropriate for a broad class of growing systems, and suggest that time-dependent algorithms that are based on the temporal linking patterns of these systems are needed to better rank the nodes.

  10. Neophilia Ranking of Scientific Journals.

    Science.gov (United States)

    Packalen, Mikko; Bhattacharya, Jay

    2017-01-01

    The ranking of scientific journals is important because of the signal it sends to scientists about what is considered most vital for scientific progress. Existing ranking systems focus on measuring the influence of a scientific paper (citations)-these rankings do not reward journals for publishing innovative work that builds on new ideas. We propose an alternative ranking based on the proclivity of journals to publish papers that build on new ideas, and we implement this ranking via a text-based analysis of all published biomedical papers dating back to 1946. In addition, we compare our neophilia ranking to citation-based (impact factor) rankings; this comparison shows that the two ranking approaches are distinct. Prior theoretical work suggests an active role for our neophilia index in science policy. Absent an explicit incentive to pursue novel science, scientists underinvest in innovative work because of a coordination problem: for work on a new idea to flourish, many scientists must decide to adopt it in their work. Rankings that are based purely on influence thus do not provide sufficient incentives for publishing innovative work. By contrast, adoption of the neophilia index as part of journal-ranking procedures by funding agencies and university administrators would provide an explicit incentive for journals to publish innovative work and thus help solve the coordination problem by increasing scientists' incentives to pursue innovative work.

  11. Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection

    KAUST Repository

    Chen, Lisha

    2012-12-01

    The reduced-rank regression is an effective method in predicting multiple response variables from the same set of predictor variables. It reduces the number of model parameters and takes advantage of interrelations between the response variables and hence improves predictive accuracy. We propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty. We apply a group-lasso type penalty that treats each row of the matrix of the regression coefficients as a group and show that this penalty satisfies certain desirable invariance properties. We develop two numerical algorithms to solve the penalized regression problem and establish the asymptotic consistency of the proposed method. In particular, the manifold structure of the reduced-rank regression coefficient matrix is considered and studied in our theoretical analysis. In our simulation study and real data analysis, the new method is compared with several existing variable selection methods for multivariate regression and exhibits competitive performance in prediction and variable selection. © 2012 American Statistical Association.

  12. A MULTIVARIATE WEIBULL DISTRIBUTION

    Directory of Open Access Journals (Sweden)

    Cheng Lee

    2010-07-01

    Full Text Available A multivariate survival function of Weibull Distribution is developed by expanding the theorem by Lu and Bhattacharyya. From the survival function, the probability density function, the cumulative probability function, the determinant of the Jacobian Matrix, and the general moment are derived.

  13. Multivariate realised kernels

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole; Hansen, Peter Reinhard; Lunde, Asger

    We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator...

  14. Low-rank coal research

    Energy Technology Data Exchange (ETDEWEB)

    Weber, G. F.; Laudal, D. L.

    1989-01-01

    This work is a compilation of reports on ongoing research at the University of North Dakota. Topics include: Control Technology and Coal Preparation Research (SO{sub x}/NO{sub x} control, waste management), Advanced Research and Technology Development (turbine combustion phenomena, combustion inorganic transformation, coal/char reactivity, liquefaction reactivity of low-rank coals, gasification ash and slag characterization, fine particulate emissions), Combustion Research (fluidized bed combustion, beneficiation of low-rank coals, combustion characterization of low-rank coal fuels, diesel utilization of low-rank coals), Liquefaction Research (low-rank coal direct liquefaction), and Gasification Research (hydrogen production from low-rank coals, advanced wastewater treatment, mild gasification, color and residual COD removal from Synfuel wastewaters, Great Plains Gasification Plant, gasifier optimization).

  15. Ranking Specific Sets of Objects.

    Science.gov (United States)

    Maly, Jan; Woltran, Stefan

    2017-01-01

    Ranking sets of objects based on an order between the single elements has been thoroughly studied in the literature. In particular, it has been shown that it is in general impossible to find a total ranking - jointly satisfying properties as dominance and independence - on the whole power set of objects. However, in many applications certain elements from the entire power set might not be required and can be neglected in the ranking process. For instance, certain sets might be ruled out due to hard constraints or are not satisfying some background theory. In this paper, we treat the computational problem whether an order on a given subset of the power set of elements satisfying different variants of dominance and independence can be found, given a ranking on the elements. We show that this problem is tractable for partial rankings and NP-complete for total rankings.

  16. Statistical identification of effective input variables

    International Nuclear Information System (INIS)

    Vaurio, J.K.

    1982-09-01

    A statistical sensitivity analysis procedure has been developed for ranking the input data of large computer codes in the order of sensitivity-importance. The method is economical for large codes with many input variables, since it uses a relatively small number of computer runs. No prior judgemental elimination of input variables is needed. The sceening method is based on stagewise correlation and extensive regression analysis of output values calculated with selected input value combinations. The regression process deals with multivariate nonlinear functions, and statistical tests are also available for identifying input variables that contribute to threshold effects, i.e., discontinuities in the output variables. A computer code SCREEN has been developed for implementing the screening techniques. The efficiency has been demonstrated by several examples and applied to a fast reactor safety analysis code (Venus-II). However, the methods and the coding are general and not limited to such applications

  17. Semiparametric Gaussian copula models : Geometry and efficient rank-based estimation

    NARCIS (Netherlands)

    Segers, J.; van den Akker, R.; Werker, B.J.M.

    2014-01-01

    We propose, for multivariate Gaussian copula models with unknown margins and structured correlation matrices, a rank-based, semiparametrically efficient estimator for the Euclidean copula parameter. This estimator is defined as a one-step update of a rank-based pilot estimator in the direction of

  18. The value of multivariate model sophistication

    DEFF Research Database (Denmark)

    Rombouts, Jeroen; Stentoft, Lars; Violante, Francesco

    2014-01-01

    We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their spec....... In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances.......We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ...

  19. Multivariate survival analysis and competing risks

    CERN Document Server

    Crowder, Martin J

    2012-01-01

    Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

  20. Rank diversity of languages: generic behavior in computational linguistics.

    Science.gov (United States)

    Cocho, Germinal; Flores, Jorge; Gershenson, Carlos; Pineda, Carlos; Sánchez, Sergio

    2015-01-01

    Statistical studies of languages have focused on the rank-frequency distribution of words. Instead, we introduce here a measure of how word ranks change in time and call this distribution rank diversity. We calculate this diversity for books published in six European languages since 1800, and find that it follows a universal lognormal distribution. Based on the mean and standard deviation associated with the lognormal distribution, we define three different word regimes of languages: "heads" consist of words which almost do not change their rank in time, "bodies" are words of general use, while "tails" are comprised by context-specific words and vary their rank considerably in time. The heads and bodies reflect the size of language cores identified by linguists for basic communication. We propose a Gaussian random walk model which reproduces the rank variation of words in time and thus the diversity. Rank diversity of words can be understood as the result of random variations in rank, where the size of the variation depends on the rank itself. We find that the core size is similar for all languages studied.

  1. Rank Diversity of Languages: Generic Behavior in Computational Linguistics

    Science.gov (United States)

    Cocho, Germinal; Flores, Jorge; Gershenson, Carlos; Pineda, Carlos; Sánchez, Sergio

    2015-01-01

    Statistical studies of languages have focused on the rank-frequency distribution of words. Instead, we introduce here a measure of how word ranks change in time and call this distribution rank diversity. We calculate this diversity for books published in six European languages since 1800, and find that it follows a universal lognormal distribution. Based on the mean and standard deviation associated with the lognormal distribution, we define three different word regimes of languages: “heads” consist of words which almost do not change their rank in time, “bodies” are words of general use, while “tails” are comprised by context-specific words and vary their rank considerably in time. The heads and bodies reflect the size of language cores identified by linguists for basic communication. We propose a Gaussian random walk model which reproduces the rank variation of words in time and thus the diversity. Rank diversity of words can be understood as the result of random variations in rank, where the size of the variation depends on the rank itself. We find that the core size is similar for all languages studied. PMID:25849150

  2. Ranking nodes in growing networks: When PageRank fails

    Science.gov (United States)

    Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng

    2015-11-01

    PageRank is arguably the most popular ranking algorithm which is being applied in real systems ranging from information to biological and infrastructure networks. Despite its outstanding popularity and broad use in different areas of science, the relation between the algorithm’s efficacy and properties of the network on which it acts has not yet been fully understood. We study here PageRank’s performance on a network model supported by real data, and show that realistic temporal effects make PageRank fail in individuating the most valuable nodes for a broad range of model parameters. Results on real data are in qualitative agreement with our model-based findings. This failure of PageRank reveals that the static approach to information filtering is inappropriate for a broad class of growing systems, and suggest that time-dependent algorithms that are based on the temporal linking patterns of these systems are needed to better rank the nodes.

  3. Multivariate wavelet frames

    CERN Document Server

    Skopina, Maria; Protasov, Vladimir

    2016-01-01

    This book presents a systematic study of multivariate wavelet frames with matrix dilation, in particular, orthogonal and bi-orthogonal bases, which are a special case of frames. Further, it provides algorithmic methods for the construction of dual and tight wavelet frames with a desirable approximation order, namely compactly supported wavelet frames, which are commonly required by engineers. It particularly focuses on methods of constructing them. Wavelet bases and frames are actively used in numerous applications such as audio and graphic signal processing, compression and transmission of information. They are especially useful in image recovery from incomplete observed data due to the redundancy of frame systems. The construction of multivariate wavelet frames, especially bases, with desirable properties remains a challenging problem as although a general scheme of construction is well known, its practical implementation in the multidimensional setting is difficult. Another important feature of wavelet is ...

  4. Multivariate calculus and geometry

    CERN Document Server

    Dineen, Seán

    2014-01-01

    Multivariate calculus can be understood best by combining geometric insight, intuitive arguments, detailed explanations and mathematical reasoning. This textbook has successfully followed this programme. It additionally provides a solid description of the basic concepts, via familiar examples, which are then tested in technically demanding situations. In this new edition the introductory chapter and two of the chapters on the geometry of surfaces have been revised. Some exercises have been replaced and others provided with expanded solutions. Familiarity with partial derivatives and a course in linear algebra are essential prerequisites for readers of this book. Multivariate Calculus and Geometry is aimed primarily at higher level undergraduates in the mathematical sciences. The inclusion of many practical examples involving problems of several variables will appeal to mathematics, science and engineering students.

  5. Intelligent multivariate process supervision

    International Nuclear Information System (INIS)

    Visuri, Pertti.

    1986-01-01

    This thesis addresses the difficulties encountered in managing large amounts of data in supervisory control of complex systems. Some previous alarm and disturbance analysis concepts are reviewed and a method for improving the supervision of complex systems is presented. The method, called multivariate supervision, is based on adding low level intelligence to the process control system. By using several measured variables linked together by means of deductive logic, the system can take into account the overall state of the supervised system. Thus, it can present to the operators fewer messages with higher information content than the conventional control systems which are based on independent processing of each variable. In addition, the multivariate method contains a special information presentation concept for improving the man-machine interface. (author)

  6. Multivariate rational data fitting

    Science.gov (United States)

    Cuyt, Annie; Verdonk, Brigitte

    1992-12-01

    Sections 1 and 2 discuss the advantages of an object-oriented implementation combined with higher floating-point arithmetic, of the algorithms available for multivariate data fitting using rational functions. Section 1 will in particular explain what we mean by "higher arithmetic". Section 2 will concentrate on the concepts of "object orientation". In sections 3 and 4 we shall describe the generality of the data structure that can be dealt with: due to some new results virtually every data set is acceptable right now, with possible coalescence of coordinates or points. In order to solve the multivariate rational interpolation problem the data sets are fed to different algorithms depending on the structure of the interpolation points in then-variate space.

  7. On Multivariate Methods in Robust Econometrics

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan

    2012-01-01

    Roč. 21, č. 1 (2012), s. 69-82 ISSN 1210-0455 R&D Projects: GA MŠk(CZ) 1M06014 Institutional research plan: CEZ:AV0Z10300504 Keywords : least weighted squares * heteroscedasticity * multivariate statistics * model selection * diagnostics * computational aspects Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.561, year: 2012 http://www.vse.cz/pep/abstrakt.php?IDcl=411

  8. Transient multivariable sensor evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Vilim, Richard B.; Heifetz, Alexander

    2017-02-21

    A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.

  9. PageRank tracker: from ranking to tracking.

    Science.gov (United States)

    Gong, Chen; Fu, Keren; Loza, Artur; Wu, Qiang; Liu, Jia; Yang, Jie

    2014-06-01

    Video object tracking is widely used in many real-world applications, and it has been extensively studied for over two decades. However, tracking robustness is still an issue in most existing methods, due to the difficulties with adaptation to environmental or target changes. In order to improve adaptability, this paper formulates the tracking process as a ranking problem, and the PageRank algorithm, which is a well-known webpage ranking algorithm used by Google, is applied. Labeled and unlabeled samples in tracking application are analogous to query webpages and the webpages to be ranked, respectively. Therefore, determining the target is equivalent to finding the unlabeled sample that is the most associated with existing labeled set. We modify the conventional PageRank algorithm in three aspects for tracking application, including graph construction, PageRank vector acquisition and target filtering. Our simulations with the use of various challenging public-domain video sequences reveal that the proposed PageRank tracker outperforms mean-shift tracker, co-tracker, semiboosting and beyond semiboosting trackers in terms of accuracy, robustness and stability.

  10. A Statistical Analysis of Cointegration for I(2) Variables

    DEFF Research Database (Denmark)

    Johansen, Søren

    1995-01-01

    be conducted using the ¿ sup2/sup distribution. It is shown to what extent inference on the cointegration ranks can be conducted using the tables already prepared for the analysis of cointegration of I(1) variables. New tables are needed for the test statistics to control the size of the tests. This paper...... contains a multivariate test for the existence of I(2) variables. This test is illustrated using a data set consisting of U.K. and foreign prices and interest rates as well as the exchange rate....

  11. Control Multivariable por Desacoplo

    Directory of Open Access Journals (Sweden)

    Fernando Morilla

    2013-01-01

    Full Text Available Resumen: La interacción entre variables es una característica inherente de los procesos multivariables, que dificulta su operación y el diseño de sus sistemas de control. Bajo el paradigma de Control por desacoplo se agrupan un conjunto de metodologías, que tradicionalmente han estado orientadas a eliminar o reducir la interacción, y que recientemente algunos investigadores han reorientado con objetivos de solucionar un problema tan complejo como es el control multivariable. Parte del material descrito en este artículo es bien conocido en el campo del control de procesos, pero la mayor parte de él son resultados de varios años de investigación de los autores en los que han primado la generalización del problema, la búsqueda de soluciones de fácil implementación y la combinación de bloques elementales de control PID. Esta conjunción de intereses provoca que no siempre se pueda conseguir un desacoplo perfecto, pero que sí se pueda conseguir una considerable reducción de la interacción en el nivel básico de la pirámide de control, en beneficio de otros sistemas de control que ocupan niveles jerárquicos superiores. El artículo resume todos los aspectos básicos del Control por desacoplo y su aplicación a dos procesos representativos: una planta experimental de cuatro tanques acoplados y un modelo 4×4 de un sistema experimental de calefacción, ventilación y aire acondicionado. Abstract: The interaction between variables is inherent in multivariable processes and this fact may complicate their operation and control system design. Under the paradigm of decoupling control, several methodologies that traditionally have been addressed to cancel or reduce the interactions are gathered. Recently, this approach has been reoriented by several researchers with the aim to solve such a complex problem as the multivariable control. Parts of the material in this work are well known in the process control field; however, most of them are

  12. Multivariate realised kernels

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger

    2011-01-01

    We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement error of certain types and can also handle non-synchronous trading. It is the first estimator...... which has these three properties which are all essential for empirical work in this area. We derive the large sample asymptotics of this estimator and assess its accuracy using a Monte Carlo study. We implement the estimator on some US equity data, comparing our results to previous work which has used...

  13. Implementation of quality by design principles in the development of microsponges as drug delivery carriers: Identification and optimization of critical factors using multivariate statistical analyses and design of experiments studies.

    Science.gov (United States)

    Simonoska Crcarevska, Maja; Dimitrovska, Aneta; Sibinovska, Nadica; Mladenovska, Kristina; Slavevska Raicki, Renata; Glavas Dodov, Marija

    2015-07-15

    Microsponges drug delivery system (MDDC) was prepared by double emulsion-solvent-diffusion technique using rotor-stator homogenization. Quality by design (QbD) concept was implemented for the development of MDDC with potential to be incorporated into semisolid dosage form (gel). Quality target product profile (QTPP) and critical quality attributes (CQA) were defined and identified, accordingly. Critical material attributes (CMA) and Critical process parameters (CPP) were identified using quality risk management (QRM) tool, failure mode, effects and criticality analysis (FMECA). CMA and CPP were identified based on results obtained from principal component analysis (PCA-X&Y) and partial least squares (PLS) statistical analysis along with literature data, product and process knowledge and understanding. FMECA identified amount of ethylcellulose, chitosan, acetone, dichloromethane, span 80, tween 80 and water ratio in primary/multiple emulsions as CMA and rotation speed and stirrer type used for organic solvent removal as CPP. The relationship between identified CPP and particle size as CQA was described in the design space using design of experiments - one-factor response surface method. Obtained results from statistically designed experiments enabled establishment of mathematical models and equations that were used for detailed characterization of influence of identified CPP upon MDDC particle size and particle size distribution and their subsequent optimization. Copyright © 2015 Elsevier B.V. All rights reserved.

  14. Universal scaling in sports ranking

    International Nuclear Information System (INIS)

    Deng Weibing; Li Wei; Cai Xu; Bulou, Alain; Wang Qiuping A

    2012-01-01

    Ranking is a ubiquitous phenomenon in human society. On the web pages of Forbes, one may find all kinds of rankings, such as the world's most powerful people, the world's richest people, the highest-earning tennis players, and so on and so forth. Herewith, we study a specific kind—sports ranking systems in which players' scores and/or prize money are accrued based on their performances in different matches. By investigating 40 data samples which span 12 different sports, we find that the distributions of scores and/or prize money follow universal power laws, with exponents nearly identical for most sports. In order to understand the origin of this universal scaling we focus on the tennis ranking systems. By checking the data we find that, for any pair of players, the probability that the higher-ranked player tops the lower-ranked opponent is proportional to the rank difference between the pair. Such a dependence can be well fitted to a sigmoidal function. By using this feature, we propose a simple toy model which can simulate the competition of players in different matches. The simulations yield results consistent with the empirical findings. Extensive simulation studies indicate that the model is quite robust with respect to the modifications of some parameters. (paper)

  15. Multivariate Process Control with Autocorrelated Data

    DEFF Research Database (Denmark)

    Kulahci, Murat

    2011-01-01

    As sensor and computer technology continues to improve, it becomes a normal occurrence that we confront with high dimensional data sets. As in many areas of industrial statistics, this brings forth various challenges in statistical process control and monitoring. This new high dimensional data...... often exhibit not only cross-­‐correlation among the quality characteristics of interest but also serial dependence as a consequence of high sampling frequency and system dynamics. In practice, the most common method of monitoring multivariate data is through what is called the Hotelling’s T2 statistic....... In this paper, we discuss the effect of autocorrelation (when it is ignored) on multivariate control charts based on these methods and provide some practical suggestions and remedies to overcome this problem....

  16. Multivariate data analysis

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, A.A.; Antony, M.K.; Somayajulu, Y.K.; Sarma, Y.V.B.; Almeida, A.M.; Mahadevan, R.

    , Head Applied Statistics Unit, Indian Statistical Institute, Calcutta for going through the section on Canonical Correlation Analysis and offering his comments on the same. This report has been prepared using ?Latex? on a ?Linux? platform, viz...., the personal computer Kapila. I wish to thank Mr. Dattaram Shivji for installing ?Latex? and ?GMT? packages on the personal computer. The style file used for preparing this report, has been hacked by me from a Goa University, Ph. D style file prepared by Dr. D...

  17. Determination of the archaeological origin of ceramic fragments characterized by neutron activation analysis, by means of the application of multivariable statistical analysis techniques;Determinacion del origen de fragmentos de ceramica arqueologica caracterizados con analisis por activacion neutronica, mediante la aplicacion de tecnicas de analisis estadistico multivariable

    Energy Technology Data Exchange (ETDEWEB)

    Almazan T, M. G.; Jimenez R, M.; Monroy G, F.; Tenorio, D. [ININ, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico); Rodriguez G, N. L. [Instituto Mexiquense de Cultura, Subdireccion de Restauracion y Conservacion, Hidalgo poniente No. 1013, 50080 Toluca, Estado de Mexico (Mexico)

    2009-07-01

    The elementary composition of archaeological ceramic fragments obtained during the explorations in San Miguel Ixtapan, Mexico State, was determined by the neutron activation analysis technique. The samples irradiation was realized in the research reactor TRIGA Mark III with a neutrons flow of 1centre dot10{sup 13}ncentre dotcm{sup -2}centre dots{sup -1}. The irradiation time was of 2 hours. Previous to the acquisition of the gamma rays spectrum the samples were allowed to decay from 12 to 14 days. The analyzed elements were: Nd, Ce, Lu, Eu, Yb, Pa(Th), Tb, La, Cr, Hf, Sc, Co, Fe, Cs, Rb. The statistical treatment of the data, consistent in the group analysis and the main components analysis allowed to identify three different origins of the archaeological ceramic, designated as: local, foreign and regional. (Author)

  18. Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors

    KAUST Repository

    Sang, Huiyan

    2011-12-01

    This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a nonseparable and nonstationary cross-covariance structure. We also present a covariance approximation approach to facilitate the computation in the modeling and analysis of very large multivariate spatial data sets. The covariance approximation consists of two parts: a reduced-rank part to capture the large-scale spatial dependence, and a sparse covariance matrix to correct the small-scale dependence error induced by the reduced rank approximation. We pay special attention to the case that the second part of the approximation has a block-diagonal structure. Simulation results of model fitting and prediction show substantial improvement of the proposed approximation over the predictive process approximation and the independent blocks analysis. We then apply our computational approach to the joint statistical modeling of multiple climate model errors. © 2012 Institute of Mathematical Statistics.

  19. PageRank of integers

    International Nuclear Information System (INIS)

    Frahm, K M; Shepelyansky, D L; Chepelianskii, A D

    2012-01-01

    We up a directed network tracing links from a given integer to its divisors and analyze the properties of the Google matrix of this network. The PageRank vector of this matrix is computed numerically and it is shown that its probability is approximately inversely proportional to the PageRank index thus being similar to the Zipf law and the dependence established for the World Wide Web. The spectrum of the Google matrix of integers is characterized by a large gap and a relatively small number of nonzero eigenvalues. A simple semi-analytical expression for the PageRank of integers is derived that allows us to find this vector for matrices of billion size. This network provides a new PageRank order of integers. (paper)

  20. Multivariable calculus with applications

    CERN Document Server

    Lax, Peter D

    2017-01-01

    This text in multivariable calculus fosters comprehension through meaningful explanations. Written with students in mathematics, the physical sciences, and engineering in mind, it extends concepts from single variable calculus such as derivative, integral, and important theorems to partial derivatives, multiple integrals, Stokes’ and divergence theorems. Students with a background in single variable calculus are guided through a variety of problem solving techniques and practice problems. Examples from the physical sciences are utilized to highlight the essential relationship between calculus and modern science. The symbiotic relationship between science and mathematics is shown by deriving and discussing several conservation laws, and vector calculus is utilized to describe a number of physical theories via partial differential equations. Students will learn that mathematics is the language that enables scientific ideas to be precisely formulated and that science is a source for the development of mathemat...

  1. Freudenthal ranks: GHZ versus W

    International Nuclear Information System (INIS)

    Borsten, L

    2013-01-01

    The Hilbert space of three-qubit pure states may be identified with a Freudenthal triple system. Every state has an unique Freudenthal rank ranging from 1 to 4, which is determined by a set of automorphism group covariants. It is shown here that the optimal success rates for winning a three-player non-local game, varying over all local strategies, are strictly ordered by the Freudenthal rank of the shared three-qubit resource. (paper)

  2. Ranking Queries on Uncertain Data

    CERN Document Server

    Hua, Ming

    2011-01-01

    Uncertain data is inherent in many important applications, such as environmental surveillance, market analysis, and quantitative economics research. Due to the importance of those applications and rapidly increasing amounts of uncertain data collected and accumulated, analyzing large collections of uncertain data has become an important task. Ranking queries (also known as top-k queries) are often natural and useful in analyzing uncertain data. Ranking Queries on Uncertain Data discusses the motivations/applications, challenging problems, the fundamental principles, and the evaluation algorith

  3. Ranking in evolving complex networks

    Science.gov (United States)

    Liao, Hao; Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng; Zhou, Ming-Yang

    2017-05-01

    Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Many popular ranking algorithms (such as Google's PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. At the same time, recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of network traffic, prediction of future links, and identification of significant nodes.

  4. A MULTIVARIATE ANALYSIS OF CROATIAN COUNTIES ENTREPRENEURSHIP

    Directory of Open Access Journals (Sweden)

    Elza Jurun

    2012-12-01

    Full Text Available In the focus of this paper is a multivariate analysis of Croatian Counties entrepreneurship. Complete data base available by official statistic institutions at national and regional level is used. Modern econometric methodology starting from a comparative analysis via multiple regression to multivariate cluster analysis is carried out as well as the analysis of successful or inefficacious entrepreneurship measured by indicators of efficiency, profitability and productivity. Time horizons of the comparative analysis are in 2004 and 2010. Accelerators of socio-economic development - number of entrepreneur investors, investment in fixed assets and current assets ratio in multiple regression model are analytically filtered between twenty-six independent variables as variables of the dominant influence on GDP per capita in 2010 as dependent variable. Results of multivariate cluster analysis of twentyone Croatian Counties are interpreted also in the sense of three Croatian NUTS 2 regions according to European nomenclature of regional territorial division of Croatia.

  5. Simplicial band depth for multivariate functional data

    KAUST Repository

    López-Pintado, Sara

    2014-03-05

    We propose notions of simplicial band depth for multivariate functional data that extend the univariate functional band depth. The proposed simplicial band depths provide simple and natural criteria to measure the centrality of a trajectory within a sample of curves. Based on these depths, a sample of multivariate curves can be ordered from the center outward and order statistics can be defined. Properties of the proposed depths, such as invariance and consistency, can be established. A simulation study shows the robustness of this new definition of depth and the advantages of using a multivariate depth versus the marginal depths for detecting outliers. Real data examples from growth curves and signature data are used to illustrate the performance and usefulness of the proposed depths. © 2014 Springer-Verlag Berlin Heidelberg.

  6. International Conference on Robust Rank-Based and Nonparametric Methods

    CERN Document Server

    McKean, Joseph

    2016-01-01

    The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of the models used in practice including linear, generalized linear, mixed, and nonlinear models. Settings are both multivariate and univariate. With the development of R packages in these areas, computation of these procedures is easily shared with r...

  7. Rankings & Estimates: Rankings of the States 2016 and Estimates of School Statistics 2017

    Science.gov (United States)

    National Education Association, 2017

    2017-01-01

    The data presented in this combined report provide facts about the extent to which local, state, and national governments commit resources to public education. NEA Research offers this report to its state and local affiliates as well as to researchers, policymakers, and the public as a tool to examine public education policies, programs, and…

  8. RANK and RANK ligand expression in primary human osteosarcoma

    Directory of Open Access Journals (Sweden)

    Daniel Branstetter

    2015-09-01

    Our results demonstrate RANKL expression was observed in the tumor element in 68% of human OS using IHC. However, the staining intensity was relatively low and only 37% (29/79 of samples exhibited≥10% RANKL positive tumor cells. RANK expression was not observed in OS tumor cells. In contrast, RANK expression was clearly observed in other cells within OS samples, including the myeloid osteoclast precursor compartment, osteoclasts and in giant osteoclast cells. The intensity and frequency of RANKL and RANK staining in OS samples were substantially less than that observed in GCTB samples. The observation that RANKL is expressed in OS cells themselves suggests that these tumors may mediate an osteoclastic response, and anti-RANKL therapy may potentially be protective against bone pathologies in OS. However, the absence of RANK expression in primary human OS cells suggests that any autocrine RANKL/RANK signaling in human OS tumor cells is not operative, and anti-RANKL therapy would not directly affect the tumor.

  9. Ranking structures and rank-rank correlations of countries: The FIFA and UEFA cases

    Science.gov (United States)

    Ausloos, Marcel; Cloots, Rudi; Gadomski, Adam; Vitanov, Nikolay K.

    2014-04-01

    Ranking of agents competing with each other in complex systems may lead to paradoxes according to the pre-chosen different measures. A discussion is presented on such rank-rank, similar or not, correlations based on the case of European countries ranked by UEFA and FIFA from different soccer competitions. The first question to be answered is whether an empirical and simple law is obtained for such (self-) organizations of complex sociological systems with such different measuring schemes. It is found that the power law form is not the best description contrary to many modern expectations. The stretched exponential is much more adequate. Moreover, it is found that the measuring rules lead to some inner structures in both cases.

  10. The analysis of multivariate group differences using common principal components

    NARCIS (Netherlands)

    Bechger, T.M.; Blanca, M.J.; Maris, G.

    2014-01-01

    Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences

  11. An overview of multivariate gamma distributions as seen from a (multivariate) matrix exponential perspective

    DEFF Research Database (Denmark)

    Bladt, Mogens; Nielsen, Bo Friis

    2012-01-01

    Laplace transform. In a longer perspective stochastic and statistical analysis for MVME will in particular apply to any of the previously defined distributions. Multivariate gamma distributions have been used in a variety of fields like hydrology, [11], [10], [6], space (wind modeling) [9] reliability [3......Numerous definitions of multivariate exponential and gamma distributions can be retrieved from the literature [4]. These distribtuions belong to the class of Multivariate Matrix-- Exponetial Distributions (MVME) whenever their joint Laplace transform is a rational function. The majority...... of these distributions further belongs to an important subclass of MVME distributions [5, 1] where the multivariate random vector can be interpreted as a number of simultaneously collected rewards during sojourns in a the states of a Markov chain with one absorbing state, the rest of the states being transient. We...

  12. Multivariate Methods Based Soft Measurement for Wine Quality Evaluation

    Directory of Open Access Journals (Sweden)

    Shen Yin

    2014-01-01

    a decision. However, since the physicochemical indexes of wine can to some extent reflect the quality of wine, the multivariate statistical methods based soft measure can help the oenologist in wine evaluation.

  13. Multivariate Regression of Liver on Intestine of Mice: A ...

    African Journals Online (AJOL)

    Multivariate Regression of Liver on Intestine of Mice: A Chemotherapeutic Evaluation of Plant ... Using an analysis of covariance model, the effects ... The findings revealed, with the aid of likelihood-ratio statistic, a marked improvement in

  14. Power Estimation in Multivariate Analysis of Variance

    Directory of Open Access Journals (Sweden)

    Jean François Allaire

    2007-09-01

    Full Text Available Power is often overlooked in designing multivariate studies for the simple reason that it is believed to be too complicated. In this paper, it is shown that power estimation in multivariate analysis of variance (MANOVA can be approximated using a F distribution for the three popular statistics (Hotelling-Lawley trace, Pillai-Bartlett trace, Wilk`s likelihood ratio. Consequently, the same procedure, as in any statistical test, can be used: computation of the critical F value, computation of the noncentral parameter (as a function of the effect size and finally estimation of power using a noncentral F distribution. Various numerical examples are provided which help to understand and to apply the method. Problems related to post hoc power estimation are discussed.

  15. Power-law and exponential rank distributions: A panoramic Gibbsian perspective

    International Nuclear Information System (INIS)

    Eliazar, Iddo

    2015-01-01

    Rank distributions are collections of positive sizes ordered either increasingly or decreasingly. Many decreasing rank distributions, formed by the collective collaboration of human actions, follow an inverse power-law relation between ranks and sizes. This remarkable empirical fact is termed Zipf’s law, and one of its quintessential manifestations is the demography of human settlements — which exhibits a harmonic relation between ranks and sizes. In this paper we present a comprehensive statistical-physics analysis of rank distributions, establish that power-law and exponential rank distributions stand out as optimal in various entropy-based senses, and unveil the special role of the harmonic relation between ranks and sizes. Our results extend the contemporary entropy-maximization view of Zipf’s law to a broader, panoramic, Gibbsian perspective of increasing and decreasing power-law and exponential rank distributions — of which Zipf’s law is one out of four pillars

  16. Power-law and exponential rank distributions: A panoramic Gibbsian perspective

    Energy Technology Data Exchange (ETDEWEB)

    Eliazar, Iddo, E-mail: eliazar@post.tau.ac.il

    2015-04-15

    Rank distributions are collections of positive sizes ordered either increasingly or decreasingly. Many decreasing rank distributions, formed by the collective collaboration of human actions, follow an inverse power-law relation between ranks and sizes. This remarkable empirical fact is termed Zipf’s law, and one of its quintessential manifestations is the demography of human settlements — which exhibits a harmonic relation between ranks and sizes. In this paper we present a comprehensive statistical-physics analysis of rank distributions, establish that power-law and exponential rank distributions stand out as optimal in various entropy-based senses, and unveil the special role of the harmonic relation between ranks and sizes. Our results extend the contemporary entropy-maximization view of Zipf’s law to a broader, panoramic, Gibbsian perspective of increasing and decreasing power-law and exponential rank distributions — of which Zipf’s law is one out of four pillars.

  17. Multivariate Welch t-test on distances

    OpenAIRE

    Alekseyenko, Alexander V.

    2016-01-01

    Motivation: Permutational non-Euclidean analysis of variance, PERMANOVA, is routinely used in exploratory analysis of multivariate datasets to draw conclusions about the significance of patterns visualized through dimension reduction. This method recognizes that pairwise distance matrix between observations is sufficient to compute within and between group sums of squares necessary to form the (pseudo) F statistic. Moreover, not only Euclidean, but arbitrary distances can be used. This method...

  18. Diffusion of scientific credits and the ranking of scientists

    Science.gov (United States)

    Radicchi, Filippo; Fortunato, Santo; Markines, Benjamin; Vespignani, Alessandro

    2009-11-01

    Recently, the abundance of digital data is enabling the implementation of graph-based ranking algorithms that provide system level analysis for ranking publications and authors. Here, we take advantage of the entire Physical Review publication archive (1893-2006) to construct authors’ networks where weighted edges, as measured from opportunely normalized citation counts, define a proxy for the mechanism of scientific credit transfer. On this network, we define a ranking method based on a diffusion algorithm that mimics the spreading of scientific credits on the network. We compare the results obtained with our algorithm with those obtained by local measures such as the citation count and provide a statistical analysis of the assignment of major career awards in the area of physics. A website where the algorithm is made available to perform customized rank analysis can be found at the address http://www.physauthorsrank.org.

  19. Toward optimal feature selection using ranking methods and classification algorithms

    Directory of Open Access Journals (Sweden)

    Novaković Jasmina

    2011-01-01

    Full Text Available We presented a comparison between several feature ranking methods used on two real datasets. We considered six ranking methods that can be divided into two broad categories: statistical and entropy-based. Four supervised learning algorithms are adopted to build models, namely, IB1, Naive Bayes, C4.5 decision tree and the RBF network. We showed that the selection of ranking methods could be important for classification accuracy. In our experiments, ranking methods with different supervised learning algorithms give quite different results for balanced accuracy. Our cases confirm that, in order to be sure that a subset of features giving the highest accuracy has been selected, the use of many different indices is recommended.

  20. Tucker Tensor analysis of Matern functions in spatial statistics

    KAUST Repository

    Litvinenko, Alexander

    2018-03-09

    In this work, we describe advanced numerical tools for working with multivariate functions and for the analysis of large data sets. These tools will drastically reduce the required computing time and the storage cost, and, therefore, will allow us to consider much larger data sets or finer meshes. Covariance matrices are crucial in spatio-temporal statistical tasks, but are often very expensive to compute and store, especially in 3D. Therefore, we approximate covariance functions by cheap surrogates in a low-rank tensor format. We apply the Tucker and canonical tensor decompositions to a family of Matern- and Slater-type functions with varying parameters and demonstrate numerically that their approximations exhibit exponentially fast convergence. We prove the exponential convergence of the Tucker and canonical approximations in tensor rank parameters. Several statistical operations are performed in this low-rank tensor format, including evaluating the conditional covariance matrix, spatially averaged estimation variance, computing a quadratic form, determinant, trace, loglikelihood, inverse, and Cholesky decomposition of a large covariance matrix. Low-rank tensor approximations reduce the computing and storage costs essentially. For example, the storage cost is reduced from an exponential O(n^d) to a linear scaling O(drn), where d is the spatial dimension, n is the number of mesh points in one direction, and r is the tensor rank. Prerequisites for applicability of the proposed techniques are the assumptions that the data, locations, and measurements lie on a tensor (axes-parallel) grid and that the covariance function depends on a distance, ||x-y||.

  1. Ranking species in mutualistic networks

    Science.gov (United States)

    Domínguez-García, Virginia; Muñoz, Miguel A.

    2015-02-01

    Understanding the architectural subtleties of ecological networks, believed to confer them enhanced stability and robustness, is a subject of outmost relevance. Mutualistic interactions have been profusely studied and their corresponding bipartite networks, such as plant-pollinator networks, have been reported to exhibit a characteristic ``nested'' structure. Assessing the importance of any given species in mutualistic networks is a key task when evaluating extinction risks and possible cascade effects. Inspired in a recently introduced algorithm -similar in spirit to Google's PageRank but with a built-in non-linearity- here we propose a method which -by exploiting their nested architecture- allows us to derive a sound ranking of species importance in mutualistic networks. This method clearly outperforms other existing ranking schemes and can become very useful for ecosystem management and biodiversity preservation, where decisions on what aspects of ecosystems to explicitly protect need to be made.

  2. University rankings in computer science

    DEFF Research Database (Denmark)

    Ehret, Philip; Zuccala, Alesia Ann; Gipp, Bela

    2017-01-01

    This is a research-in-progress paper concerning two types of institutional rankings, the Leiden and QS World ranking, and their relationship to a list of universities’ ‘geo-based’ impact scores, and Computing Research and Education Conference (CORE) participation scores in the field of computer...... science. A ‘geo-based’ impact measure examines the geographical distribution of incoming citations to a particular university’s journal articles for a specific period of time. It takes into account both the number of citations and the geographical variability in these citations. The CORE participation...... score is calculated on the basis of the number of weighted proceedings papers that a university has contributed to either an A*, A, B, or C conference as ranked by the Computing Research and Education Association of Australasia. In addition to calculating the correlations between the distinct university...

  3. Practical multivariate analysis

    CERN Document Server

    Afifi, Abdelmonem; Clark, Virginia A

    2011-01-01

    ""First of all, it is very easy to read. … The authors manage to introduce and (at least partially) explain even quite complex concepts, e.g. eigenvalues, in an easy and pedagogical way that I suppose is attractive to readers without deeper statistical knowledge. The text is also sprinkled with references for those who want to probe deeper into a certain topic. Secondly, I personally find the book's emphasis on practical data handling very appealing. … Thirdly, the book gives very nice coverage of regression analysis. … this is a nicely written book that gives a good overview of a large number

  4. It's all relative: ranking the diversity of aquatic bacterial communities.

    Science.gov (United States)

    Shaw, Allison K; Halpern, Aaron L; Beeson, Karen; Tran, Bao; Venter, J Craig; Martiny, Jennifer B H

    2008-09-01

    The study of microbial diversity patterns is hampered by the enormous diversity of microbial communities and the lack of resources to sample them exhaustively. For many questions about richness and evenness, however, one only needs to know the relative order of diversity among samples rather than total diversity. We used 16S libraries from the Global Ocean Survey to investigate the ability of 10 diversity statistics (including rarefaction, non-parametric, parametric, curve extrapolation and diversity indices) to assess the relative diversity of six aquatic bacterial communities. Overall, we found that the statistics yielded remarkably similar rankings of the samples for a given sequence similarity cut-off. This correspondence, despite the different underlying assumptions of the statistics, suggests that diversity statistics are a useful tool for ranking samples of microbial diversity. In addition, sequence similarity cut-off influenced the diversity ranking of the samples, demonstrating that diversity statistics can also be used to detect differences in phylogenetic structure among microbial communities. Finally, a subsampling analysis suggests that further sequencing from these particular clone libraries would not have substantially changed the richness rankings of the samples.

  5. Subtracting a best rank-1 approximation may increase tensor rank

    NARCIS (Netherlands)

    Stegeman, Alwin; Comon, Pierre

    2010-01-01

    It has been shown that a best rank-R approximation of an order-k tensor may not exist when R >= 2 and k >= 3. This poses a serious problem to data analysts using tensor decompositions it has been observed numerically that, generally, this issue cannot be solved by consecutively computing and

  6. Rank Dynamics of Word Usage at Multiple Scales

    Directory of Open Access Journals (Sweden)

    José A. Morales

    2018-05-01

    Full Text Available The recent dramatic increase in online data availability has allowed researchers to explore human culture with unprecedented detail, such as the growth and diversification of language. In particular, it provides statistical tools to explore whether word use is similar across languages, and if so, whether these generic features appear at different scales of language structure. Here we use the Google Books N-grams dataset to analyze the temporal evolution of word usage in several languages. We apply measures proposed recently to study rank dynamics, such as the diversity of N-grams in a given rank, the probability that an N-gram changes rank between successive time intervals, the rank entropy, and the rank complexity. Using different methods, results show that there are generic properties for different languages at different scales, such as a core of words necessary to minimally understand a language. We also propose a null model to explore the relevance of linguistic structure across multiple scales, concluding that N-gram statistics cannot be reduced to word statistics. We expect our results to be useful in improving text prediction algorithms, as well as in shedding light on the large-scale features of language use, beyond linguistic and cultural differences across human populations.

  7. Consistent ranking of volatility models

    DEFF Research Database (Denmark)

    Hansen, Peter Reinhard; Lunde, Asger

    2006-01-01

    We show that the empirical ranking of volatility models can be inconsistent for the true ranking if the evaluation is based on a proxy for the population measure of volatility. For example, the substitution of a squared return for the conditional variance in the evaluation of ARCH-type models can...... variance in out-of-sample evaluations rather than the squared return. We derive the theoretical results in a general framework that is not specific to the comparison of volatility models. Similar problems can arise in comparisons of forecasting models whenever the predicted variable is a latent variable....

  8. Podium: Ranking Data Using Mixed-Initiative Visual Analytics.

    Science.gov (United States)

    Wall, Emily; Das, Subhajit; Chawla, Ravish; Kalidindi, Bharath; Brown, Eli T; Endert, Alex

    2018-01-01

    People often rank and order data points as a vital part of making decisions. Multi-attribute ranking systems are a common tool used to make these data-driven decisions. Such systems often take the form of a table-based visualization in which users assign weights to the attributes representing the quantifiable importance of each attribute to a decision, which the system then uses to compute a ranking of the data. However, these systems assume that users are able to quantify their conceptual understanding of how important particular attributes are to a decision. This is not always easy or even possible for users to do. Rather, people often have a more holistic understanding of the data. They form opinions that data point A is better than data point B but do not necessarily know which attributes are important. To address these challenges, we present a visual analytic application to help people rank multi-variate data points. We developed a prototype system, Podium, that allows users to drag rows in the table to rank order data points based on their perception of the relative value of the data. Podium then infers a weighting model using Ranking SVM that satisfies the user's data preferences as closely as possible. Whereas past systems help users understand the relationships between data points based on changes to attribute weights, our approach helps users to understand the attributes that might inform their understanding of the data. We present two usage scenarios to describe some of the potential uses of our proposed technique: (1) understanding which attributes contribute to a user's subjective preferences for data, and (2) deconstructing attributes of importance for existing rankings. Our proposed approach makes powerful machine learning techniques more usable to those who may not have expertise in these areas.

  9. Understanding Statistics - Cancer Statistics

    Science.gov (United States)

    Annual reports of U.S. cancer statistics including new cases, deaths, trends, survival, prevalence, lifetime risk, and progress toward Healthy People targets, plus statistical summaries for a number of common cancer types.

  10. Multivariate analysis techniques

    Energy Technology Data Exchange (ETDEWEB)

    Bendavid, Josh [European Organization for Nuclear Research (CERN), Geneva (Switzerland); Fisher, Wade C. [Michigan State Univ., East Lansing, MI (United States); Junk, Thomas R. [Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)

    2016-01-01

    The end products of experimental data analysis are designed to be simple and easy to understand: hypothesis tests and measurements of parameters. But, the experimental data themselves are voluminous and complex. Furthermore, in modern collider experiments, many petabytes of data must be processed in search of rare new processes which occur together with much more copious background processes that are of less interest to the task at hand. The systematic uncertainties on the background may be larger than the expected signal in many cases. The statistical power of an analysis and its sensitivity to systematic uncertainty can therefore usually both be improved by separating signal events from background events with higher efficiency and purity.

  11. THE USE OF RANKING SAMPLING METHOD WITHIN MARKETING RESEARCH

    Directory of Open Access Journals (Sweden)

    CODRUŢA DURA

    2011-01-01

    Full Text Available Marketing and statistical literature available to practitioners provides a wide range of sampling methods that can be implemented in the context of marketing research. Ranking sampling method is based on taking apart the general population into several strata, namely into several subdivisions which are relatively homogenous regarding a certain characteristic. In fact, the sample will be composed by selecting, from each stratum, a certain number of components (which can be proportional or non-proportional to the size of the stratum until the pre-established volume of the sample is reached. Using ranking sampling within marketing research requires the determination of some relevant statistical indicators - average, dispersion, sampling error etc. To that end, the paper contains a case study which illustrates the actual approach used in order to apply the ranking sample method within a marketing research made by a company which provides Internet connection services, on a particular category of customers – small and medium enterprises.

  12. Data depth and rank-based tests for covariance and spectral density matrices

    KAUST Repository

    Chau, Joris

    2017-06-26

    In multivariate time series analysis, objects of primary interest to study cross-dependences in the time series are the autocovariance or spectral density matrices. Non-degenerate covariance and spectral density matrices are necessarily Hermitian and positive definite, and our primary goal is to develop new methods to analyze samples of such matrices. The main contribution of this paper is the generalization of the concept of statistical data depth for collections of covariance or spectral density matrices by exploiting the geometric properties of the space of Hermitian positive definite matrices as a Riemannian manifold. This allows one to naturally characterize most central or outlying matrices, but also provides a practical framework for rank-based hypothesis testing in the context of samples of covariance or spectral density matrices. First, the desired properties of a data depth function acting on the space of Hermitian positive definite matrices are presented. Second, we propose two computationally efficient pointwise and integrated data depth functions that satisfy each of these requirements. Several applications of the developed methodology are illustrated by the analysis of collections of spectral matrices in multivariate brain signal time series datasets.

  13. Data depth and rank-based tests for covariance and spectral density matrices

    KAUST Repository

    Chau, Joris; Ombao, Hernando; Sachs, Rainer von

    2017-01-01

    In multivariate time series analysis, objects of primary interest to study cross-dependences in the time series are the autocovariance or spectral density matrices. Non-degenerate covariance and spectral density matrices are necessarily Hermitian and positive definite, and our primary goal is to develop new methods to analyze samples of such matrices. The main contribution of this paper is the generalization of the concept of statistical data depth for collections of covariance or spectral density matrices by exploiting the geometric properties of the space of Hermitian positive definite matrices as a Riemannian manifold. This allows one to naturally characterize most central or outlying matrices, but also provides a practical framework for rank-based hypothesis testing in the context of samples of covariance or spectral density matrices. First, the desired properties of a data depth function acting on the space of Hermitian positive definite matrices are presented. Second, we propose two computationally efficient pointwise and integrated data depth functions that satisfy each of these requirements. Several applications of the developed methodology are illustrated by the analysis of collections of spectral matrices in multivariate brain signal time series datasets.

  14. Let Us Rank Journalism Programs

    Science.gov (United States)

    Weber, Joseph

    2014-01-01

    Unlike law, business, and medical schools, as well as universities in general, journalism schools and journalism programs have rarely been ranked. Publishers such as "U.S. News & World Report," "Forbes," "Bloomberg Businessweek," and "Washington Monthly" do not pay them much mind. What is the best…

  15. PageRank (II): Mathematics

    African Journals Online (AJOL)

    maths/stats

    ... GAUSS SEIDEL'S. NUMERICAL ALGORITHMS IN PAGE RANK ANALYSIS. ... The convergence is guaranteed, if the absolute value of the largest eigen ... improved Gauss-Seidel iteration algorithm, based on the decomposition. U. L. D. M. +. +. = ..... This corresponds to determine the eigen vector of T with eigen value 1.

  16. Notices about using elementary statistics in psychology

    OpenAIRE

    松田, 文子; 三宅, 幹子; 橋本, 優花里; 山崎, 理央; 森田, 愛子; 小嶋, 佳子

    2003-01-01

    Improper uses of elementary statistics that were often observed in beginners' manuscripts and papers were collected and better ways were suggested. This paper consists of three parts: About descriptive statistics, multivariate analyses, and statistical tests.

  17. Multiple graph regularized protein domain ranking

    KAUST Repository

    Wang, Jim Jing-Yan

    2012-11-19

    Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.

  18. Multiple graph regularized protein domain ranking

    KAUST Repository

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2012-01-01

    Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.

  19. 14 CFR 1214.1105 - Final ranking.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Final ranking. 1214.1105 Section 1214.1105... Recruitment and Selection Program § 1214.1105 Final ranking. Final rankings will be based on a combination of... preference will be included in this final ranking in accordance with applicable regulations. ...

  20. Multiple graph regularized protein domain ranking.

    Science.gov (United States)

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2012-11-19

    Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.