International Nuclear Information System (INIS)
Hirotsu, Yuko; Suzuki, Kunihiko; Takano, Kenichi; Kojima, Mitsuhiro
2000-01-01
It is essential for preventing the recurrence of human error incidents to analyze and evaluate them with the emphasis on human factor. Detailed and structured analyses of all incidents at domestic nuclear power plants (NPPs) reported during last 31 years have been conducted based on J-HPES, in which total 193 human error cases are identified. Results obtained by the analyses have been stored into the J-HPES database. In the previous study, by applying multivariate analysis to above case studies, it was suggested that there were several occurrence patterns identified of how errors occur at NPPs. It was also clarified that the causes related to each human error are different depending on age of their occurrence. This paper described the obtained results in respects of periodical transition of human error occurrence patterns. By applying multivariate analysis to the above data, it was suggested there were two types of error occurrence patterns as to each human error type. First type is common occurrence patterns, not depending on the age, and second type is the one influenced by periodical characteristics. (author)
Multivariate differential analyses of adolescents' experiences of ...
African Journals Online (AJOL)
Aggression is reasoned to be dependent on aspects such as self-concept, moral reasoning, communication, frustration tolerance and family relationships. To analyse the data from questionnaires of 101 families (95 adolescents, 95 mothers and 91 fathers) Cronbach Alpha, various consecutive first and second order factor ...
A review of multivariate analyses in imaging genetics
Directory of Open Access Journals (Sweden)
Jingyu eLiu
2014-03-01
Full Text Available Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a prior driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA, and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and
Multivariate analyses of crater parameters and the classification of craters
Siegal, B. S.; Griffiths, J. C.
1974-01-01
Multivariate analyses were performed on certain linear dimensions of six genetic types of craters. A total of 320 craters, consisting of laboratory fluidization craters, craters formed by chemical and nuclear explosives, terrestrial maars and other volcanic craters, and terrestrial meteorite impact craters, authenticated and probable, were analyzed in the first data set in terms of their mean rim crest diameter, mean interior relief, rim height, and mean exterior rim width. The second data set contained an additional 91 terrestrial craters of which 19 were of experimental percussive impact and 28 of volcanic collapse origin, and which was analyzed in terms of mean rim crest diameter, mean interior relief, and rim height. Principal component analyses were performed on the six genetic types of craters. Ninety per cent of the variation in the variables can be accounted for by two components. Ninety-nine per cent of the variation in the craters formed by chemical and nuclear explosives is explained by the first component alone.
On the return period and design in a multivariate framework
Directory of Open Access Journals (Sweden)
G. Salvadori
2011-11-01
Full Text Available Calculating return periods and design quantiles in a multivariate environment is a difficult problem: this paper tries to make the issue clear. First, we outline a possible way to introduce a consistent theoretical framework for the calculation of the return period in a multi-dimensional environment, based on Copulas and the Kendall's measure. Secondly, we introduce several approaches for the identification of suitable design events: these latter quantities are of utmost importance in practical applications, but their calculation is yet limited, due to the lack of an adequate theoretical environment where to embed the problem. Throughout the paper, a case study involving the behavior of a dam is used to illustrate the new concepts outlined in this work.
Periodic safety analyses; Les essais periodiques
Energy Technology Data Exchange (ETDEWEB)
Gouffon, A; Zermizoglou, R
1990-12-01
The IAEA Safety Guide 50-SG-S8 devoted to 'Safety Aspects of Foundations of Nuclear Power Plants' indicates that operator of a NPP should establish a program for inspection of safe operation during construction, start-up and service life of the plant for obtaining data needed for estimating the life time of structures and components. At the same time the program should ensure that the safety margins are appropriate. Periodic safety analysis are an important part of the safety inspection program. Periodic safety reports is a method for testing the whole system or a part of the safety system following the precise criteria. Periodic safety analyses are not meant for qualification of the plant components. Separate analyses are devoted to: start-up, qualification of components and materials, and aging. All these analyses are described in this presentation. The last chapter describes the experience obtained for PWR-900 and PWR-1300 units from 1986-1989.
Optimal non-periodic inspection for a multivariate degradation model
Barker, C.T.; Newby, M.J.
2009-01-01
We address the problem of determining inspection and maintenance strategy for a system whose state is described by a multivariate stochastic process. We relax and extend the usual approaches. The system state is a multivariate stochastic process, decisions are based on a performance measure defined
Evaluation of periodic safety status analyses
International Nuclear Information System (INIS)
Faber, C.; Staub, G.
1997-01-01
In order to carry out the evaluation of safety status analyses by the safety assessor within the periodical safety reviews of nuclear power plants safety goal oriented requirements have been formulated together with complementary evaluation criteria. Their application in an inter-disciplinary coopertion covering the subject areas involved facilitates a complete safety goal oriented assessment of the plant status. The procedure is outlined briefly by an example for the safety goal 'reactivity control' for BWRs. (orig.) [de
Multivariate analyses in soil microbial ecology : a new paradigm
Thioulouse, J.; Prin, Y.; Duponnois, Robin
2012-01-01
Mycorrhizal symbiosis is a key component of a sustainable soil-plant system, governing the cycles of major plant nutrients and vegetation cover. The mycorrhizosphere includes plants roots, the mycorrhizal fungi, and a complex microbial compartment. A large number of methods have been used to characterize the genetic and functional diversity of these soil microbial communities. We present here a review of the multivariate data analysis methods that have been used in 16 research articles publis...
A Cyber-Attack Detection Model Based on Multivariate Analyses
Sakai, Yuto; Rinsaka, Koichiro; Dohi, Tadashi
In the present paper, we propose a novel cyber-attack detection model based on two multivariate-analysis methods to the audit data observed on a host machine. The statistical techniques used here are the well-known Hayashi's quantification method IV and cluster analysis method. We quantify the observed qualitative audit event sequence via the quantification method IV, and collect similar audit event sequence in the same groups based on the cluster analysis. It is shown in simulation experiments that our model can improve the cyber-attack detection accuracy in some realistic cases where both normal and attack activities are intermingled.
Directory of Open Access Journals (Sweden)
B. Gräler
2013-04-01
Full Text Available Most of the hydrological and hydraulic studies refer to the notion of a return period to quantify design variables. When dealing with multiple design variables, the well-known univariate statistical analysis is no longer satisfactory, and several issues challenge the practitioner. How should one incorporate the dependence between variables? How should a multivariate return period be defined and applied in order to yield a proper design event? In this study an overview of the state of the art for estimating multivariate design events is given and the different approaches are compared. The construction of multivariate distribution functions is done through the use of copulas, given their practicality in multivariate frequency analyses and their ability to model numerous types of dependence structures in a flexible way. A synthetic case study is used to generate a large data set of simulated discharges that is used for illustrating the effect of different modelling choices on the design events. Based on different uni- and multivariate approaches, the design hydrograph characteristics of a 3-D phenomenon composed of annual maximum peak discharge, its volume, and duration are derived. These approaches are based on regression analysis, bivariate conditional distributions, bivariate joint distributions and Kendall distribution functions, highlighting theoretical and practical issues of multivariate frequency analysis. Also an ensemble-based approach is presented. For a given design return period, the approach chosen clearly affects the calculated design event, and much attention should be given to the choice of the approach used as this depends on the real-world problem at hand.
Multivariate analyses of rotator cuff pathologies in shoulder disability.
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Jan F Henseler
Full Text Available Disability of the shoulder joint is often caused by a tear in the rotator cuff (RC muscles. Four RC muscles coordinate shoulder movement and stability, among them the supraspinatus and infraspinatus muscle which are predominantly torn. The contribution of each RC muscle to tear pathology is not fully understood. We hypothesized that muscle atrophy and fatty infiltration, features of RC muscle degeneration, are predictive of superior humeral head translation and shoulder functional disability.Shoulder features, including RC muscle surface area and fatty infiltration, superior humeral translation and RC tear size were obtained from a consecutive series of Magnetic Resonance Imaging with arthrography (MRA. We investigated patients with superior (supraspinatus, n = 39 and posterosuperior (supraspinatus and infraspinatus, n = 30 RC tears, and patients with an intact RC (n = 52 as controls. The individual or combinatorial contribution of RC measures to superior humeral translation, as a sign of RC dysfunction, was investigated with univariate or multivariate models, respectively.Using the univariate model the infraspinatus surface area and fatty infiltration in both the supraspinatus and infraspinatus had a significant contribution to RC dysfunction. With the multivariate model, however, the infraspinatus surface area only affected superior humeral translation (p<0.001 and discriminated between superior and posterosuperior tears. In contrast neither tear size nor fatty infiltration of the supraspinatus or infraspinatus contributed to superior humeral translation.Our study reveals that infraspinatus atrophy has the strongest contribution to RC tear pathologies. This suggests a pivotal role for the infraspinatus in preventing shoulder disability.
Multivariate Analyses of Rotator Cuff Pathologies in Shoulder Disability
Henseler, Jan F.; Raz, Yotam; Nagels, Jochem; van Zwet, Erik W.; Raz, Vered; Nelissen, Rob G. H. H.
2015-01-01
Background Disability of the shoulder joint is often caused by a tear in the rotator cuff (RC) muscles. Four RC muscles coordinate shoulder movement and stability, among them the supraspinatus and infraspinatus muscle which are predominantly torn. The contribution of each RC muscle to tear pathology is not fully understood. We hypothesized that muscle atrophy and fatty infiltration, features of RC muscle degeneration, are predictive of superior humeral head translation and shoulder functional disability. Methods Shoulder features, including RC muscle surface area and fatty infiltration, superior humeral translation and RC tear size were obtained from a consecutive series of Magnetic Resonance Imaging with arthrography (MRA). We investigated patients with superior (supraspinatus, n = 39) and posterosuperior (supraspinatus and infraspinatus, n = 30) RC tears, and patients with an intact RC (n = 52) as controls. The individual or combinatorial contribution of RC measures to superior humeral translation, as a sign of RC dysfunction, was investigated with univariate or multivariate models, respectively. Results Using the univariate model the infraspinatus surface area and fatty infiltration in both the supraspinatus and infraspinatus had a significant contribution to RC dysfunction. With the multivariate model, however, the infraspinatus surface area only affected superior humeral translation (ptears. In contrast neither tear size nor fatty infiltration of the supraspinatus or infraspinatus contributed to superior humeral translation. Conclusion Our study reveals that infraspinatus atrophy has the strongest contribution to RC tear pathologies. This suggests a pivotal role for the infraspinatus in preventing shoulder disability. PMID:25710703
Multivariate analyses of rotator cuff pathologies in shoulder disability.
Henseler, Jan F; Raz, Yotam; Nagels, Jochem; van Zwet, Erik W; Raz, Vered; Nelissen, Rob G H H
2015-01-01
Disability of the shoulder joint is often caused by a tear in the rotator cuff (RC) muscles. Four RC muscles coordinate shoulder movement and stability, among them the supraspinatus and infraspinatus muscle which are predominantly torn. The contribution of each RC muscle to tear pathology is not fully understood. We hypothesized that muscle atrophy and fatty infiltration, features of RC muscle degeneration, are predictive of superior humeral head translation and shoulder functional disability. Shoulder features, including RC muscle surface area and fatty infiltration, superior humeral translation and RC tear size were obtained from a consecutive series of Magnetic Resonance Imaging with arthrography (MRA). We investigated patients with superior (supraspinatus, n = 39) and posterosuperior (supraspinatus and infraspinatus, n = 30) RC tears, and patients with an intact RC (n = 52) as controls. The individual or combinatorial contribution of RC measures to superior humeral translation, as a sign of RC dysfunction, was investigated with univariate or multivariate models, respectively. Using the univariate model the infraspinatus surface area and fatty infiltration in both the supraspinatus and infraspinatus had a significant contribution to RC dysfunction. With the multivariate model, however, the infraspinatus surface area only affected superior humeral translation (ppathologies. This suggests a pivotal role for the infraspinatus in preventing shoulder disability.
Multivariant analyses of trace element patterns for environmental tracking
International Nuclear Information System (INIS)
Jervis, R.E.; Ko, M.M.C.; Junliang Tian; Puling Liu
1993-01-01
Nuclear-based analytical techniques: INAA, PIXE and photon activation permit simultaneous multielemental determination of concentrations in environmental materials, which data are often found sufficiently precise and free of uncontrolled, random errors among the various elements such that the data sets can yield valuable information on elemental communality through multi-variant statistical 'factor' analysis. Characteristic factor patterns obtained in this way can provide clues to the likely sources in the environment of various components. Recent studies in three different environmental situations: solid waste incinerators , Chinese soils, and iron and steel industry, involving measurements of 30-35 elements, have yielded distinct elemental patterns or, environmental signatures, with factor loading coefficients ranging mostly in the ranges: 0.7-0.96. (author) 10 refs.; 2 figs.; 9 tabs
A MULTIVARIATE APPROACH TO ANALYSE NATIVE FOREST TREE SPECIE SEEDS
Directory of Open Access Journals (Sweden)
Alessandro Dal Col Lúcio
2006-03-01
Full Text Available This work grouped, by species, the most similar seed tree, using the variables observed in exotic forest species of theBrazilian flora of seeds collected in the Forest Research and Soil Conservation Center of Santa Maria, Rio Grande do Sul, analyzedfrom January, 1997, to march, 2003. For the cluster analysis, all the species that possessed four or more analyses per lot wereanalyzed by the hierarchical Clustering method, of the standardized Euclidian medium distance, being also a principal componentanalysis technique for reducing the number of variables. The species Callistemon speciosus, Cassia fistula, Eucalyptus grandis,Eucalyptus robusta, Eucalyptus saligna, Eucalyptus tereticornis, Delonix regia, Jacaranda mimosaefolia e Pinus elliottii presentedmore than four analyses per lot, in which the third and fourth main components explained 80% of the total variation. The clusteranalysis was efficient in the separation of the groups of all tested species, as well as the method of the main components.
Multivariate differential analyses of adolescents' experiences of aggression in families
Directory of Open Access Journals (Sweden)
Chris Myburgh
2011-01-01
Full Text Available Aggression is part of South African society and has implications for the mental health of persons living in South Africa. If parents are aggressive adolescents are also likely to be aggressive and that will impact negatively on their mental health. In this article the nature and extent of adolescents' experiences of aggression and aggressive behaviour in the family are investigated. A deductive explorative quantitative approach was followed. Aggression is reasoned to be dependent on aspects such as self-concept, moral reasoning, communication, frustration tolerance and family relationships. To analyse the data from questionnaires of 101 families (95 adolescents, 95 mothers and 91 fathers Cronbach Alpha, various consecutive first and second order factor analyses, correlations, multiple regression, MANOVA, ANOVA and Scheffè/ Dunnett tests were used. It was found that aggression correlated negatively with the independent variables; and the correlations between adolescents and their parents were significant. Regression analyses indicated that different predictors predicted aggression. Furthermore, differences between adolescents and their parents indicated that the experienced levels of aggression between adolescents and their parents were small. Implications for education are given.
Distinguishing Nonpareil marketing group almond cultivars through multivariate analyses.
Ledbetter, Craig A; Sisterson, Mark S
2013-09-01
More than 80% of the world's almonds are grown in California with several dozen almond cultivars available commercially. To facilitate promotion and sale, almond cultivars are categorized into marketing groups based on kernel shape and appearance. Several marketing groups are recognized, with the Nonpareil Marketing Group (NMG) demanding the highest prices. Placement of cultivars into the NMG is historical and no objective standards exist for deciding whether newly developed cultivars belong in the NMG. Principal component analyses (PCA) were used to identify nut and kernel characteristics best separating the 4 NMG cultivars (Nonpareil, Jeffries, Kapareil, and Milow) from a representative of the California Marketing Group (cultivar Carmel) and the Mission Marketing Group (cultivar Padre). In addition, discriminant analyses were used to determine cultivar misclassification rates between and within the marketing groups. All 19 evaluated carpological characters differed significantly among the 6 cultivars and during 2 harvest seasons. A clear distinction of NMG cultivars from representatives of the California and Mission Marketing Groups was evident from a PCA involving the 6 cultivars. Further, NMG kernels were successfully discriminated from kernels representing the California and Mission Marketing Groups with overall kernel misclassification of only 2% using 16 of the 19 evaluated characters. Pellicle luminosity was the most discriminating character, regardless of the character set used in analyses. Results provide an objective classification of NMG almond kernels, clearly distinguishing them from kernels of cultivars representing the California and Mission Marketing Groups. Journal of Food Science © 2013 Institute of Food Technologists® No claim to original US government works.
Multivariate return periods of sea storms for coastal erosion risk assessment
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S. Corbella
2012-08-01
Full Text Available The erosion of a beach depends on various storm characteristics. Ideally, the risk associated with a storm would be described by a single multivariate return period that is also representative of the erosion risk, i.e. a 100 yr multivariate storm return period would cause a 100 yr erosion return period. Unfortunately, a specific probability level may be associated with numerous combinations of storm characteristics. These combinations, despite having the same multivariate probability, may cause very different erosion outcomes. This paper explores this ambiguity problem in the context of copula based multivariate return periods and using a case study at Durban on the east coast of South Africa. Simulations were used to correlate multivariate return periods of historical events to return periods of estimated storm induced erosion volumes. In addition, the relationship of the most-likely design event (Salvadori et al., 2011 to coastal erosion was investigated. It was found that the multivariate return periods for wave height and duration had the highest correlation to erosion return periods. The most-likely design event was found to be an inadequate design method in its current form. We explore the inclusion of conditions based on the physical realizability of wave events and the use of multivariate linear regression to relate storm parameters to erosion computed from a process based model. Establishing a link between storm statistics and erosion consequences can resolve the ambiguity between multivariate storm return periods and associated erosion return periods.
Ghanbarzadeh, Mitra; Aminghafari, Mina
2015-05-01
This article studies the prediction of periodically correlated process using wavelet transform and multivariate methods with applications to climatological data. Periodically correlated processes can be reformulated as multivariate stationary processes. Considering this fact, two new prediction methods are proposed. In the first method, we use stepwise regression between the principal components of the multivariate stationary process and past wavelet coefficients of the process to get a prediction. In the second method, we propose its multivariate version without principal component analysis a priori. Also, we study a generalization of the prediction methods dealing with a deterministic trend using exponential smoothing. Finally, we illustrate the performance of the proposed methods on simulated and real climatological data (ozone amounts, flows of a river, solar radiation, and sea levels) compared with the multivariate autoregressive model. The proposed methods give good results as we expected.
Stamate, Mirela Cristina; Todor, Nicolae; Cosgarea, Marcel
2015-01-01
The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high
The intervals method: a new approach to analyse finite element outputs using multivariate statistics
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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.
The intervals method: a new approach to analyse finite element outputs using multivariate statistics
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
Lange, Johannes
2014-01-01
The purpose of this thesis is to improve the photon selection of the CMS SinglePhoton search for Supersymmetry by using multivariate analyses.The Single-Photon search aims to ﬁnd Supersymmetry (SUSY) in data taken by theCompact Muon Solenoid (CMS) detector at the Large Hadron Collider located atthe research center CERN. SUSY is an extension of the standard model of particlephysics. The search is designed for a general gauge mediation scenario, which describes the gauge mediated SUSY breaking. The analysis uses ﬁnal states with jets,at least one photon and missing transverse energy. A data-driven prediction of themultijet background is performed for the analysis. For this purpose, photon candidates have to be classiﬁed into two selections.In this thesis the usage of multivariate analyses for the photon candidate classiﬁcation is studied. The methods used are Fisher Discriminant, Boosted Decision Treesand Artiﬁcial Neural Networks. Their performance is evaluated with respect to different aspects impor...
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
Measuring social capital through multivariate analyses for the IQ-SC.
Campos, Ana Cristina Viana; Borges, Carolina Marques; Vargas, Andréa Maria Duarte; Gomes, Viviane Elisangela; Lucas, Simone Dutra; Ferreira e Ferreira, Efigênia
2015-01-20
Social capital can be viewed as a societal process that works toward the common good as well as toward the good of the collective based on trust, reciprocity, and solidarity. Our study aimed to present two multivariate statistical analyses to examine the formation of latent classes of social capital using the IQ-SC and to identify the most important factors in building an indicator of individual social capital. A cross-sectional study was conducted in 2009 among working adolescents supported by a Brazilian NGO. The sample consisted of 363 individuals, and data were collected using the World Bank Questionnaire for measuring social capital. First, the participants were grouped by a segmentation analysis using the Two Step Cluster method based on the Euclidian distance and the centroid criteria as the criteria for aggregate answers. Using specific weights for each item, discriminant analysis was used to validate the cluster analysis in an attempt to maximize the variance among the groups with respect to the variance within the clusters. "Community participation" and "trust in one's neighbors" contributed significantly to the development of the model with two distinct discriminant functions (p < 0.001). The majority of cases (95.0%) and non-cases (93.1%) were correctly classified by discriminant analysis. The two multivariate analyses (segmentation analysis and canonical discriminant analysis), used together, can be considered good choices for measuring social capital. Our results indicate that it is possible to form three social capital groups (low, medium and high) using the IQ-SC.
Decoding the complex brain: multivariate and multimodal analyses of neuroimaging data
Energy Technology Data Exchange (ETDEWEB)
Salami, Alireza
2012-07-01
Functional brain images are extraordinarily rich data sets that reveal distributed brain networks engaged in a wide variety of cognitive operations. It is a substantial challenge both to create models of cognition that mimic behavior and underlying cognitive processes and to choose a suitable analytic method to identify underlying brain networks. Most of the contemporary techniques used in analyses of functional neuroimaging data are based on univariate approaches in which single image elements (i.e. voxels) are considered to be computationally independent measures. Beyond univariate methods (e.g. statistical parametric mapping), multivariate approaches, which identify a network across all regions of the brain rather than a tessellation of regions, are potentially well suited for analyses of brain imaging data. A multivariate method (e.g. partial least squares) is a computational strategy that determines time-varying distributed patterns of the brain (as a function of a cognitive task). Compared to its univariate counterparts, a multivariate approach provides greater levels of sensitivity and reflects cooperative interactions among brain regions. Thus, by considering information across more than one measuring point, additional information on brain function can be revealed. Similarly, by considering information across more than one measuring technique, the nature of underlying cognitive processes become well-understood. Cognitive processes have been investigated in conjunction with multiple neuroimaging modalities (e.g. fMRI, sMRI, EEG, DTI), whereas the typical method has been to analyze each modality separately. Accordingly, little work has been carried out to examine the relation between different modalities. Indeed, due to the interconnected nature of brain processing, it is plausible that changes in one modality locally or distally modulate changes in another modality. This thesis focuses on multivariate and multimodal methods of image analysis applied to
Decoding the complex brain: multivariate and multimodal analyses of neuroimaging data
International Nuclear Information System (INIS)
Salami, Alireza
2012-01-01
Functional brain images are extraordinarily rich data sets that reveal distributed brain networks engaged in a wide variety of cognitive operations. It is a substantial challenge both to create models of cognition that mimic behavior and underlying cognitive processes and to choose a suitable analytic method to identify underlying brain networks. Most of the contemporary techniques used in analyses of functional neuroimaging data are based on univariate approaches in which single image elements (i.e. voxels) are considered to be computationally independent measures. Beyond univariate methods (e.g. statistical parametric mapping), multivariate approaches, which identify a network across all regions of the brain rather than a tessellation of regions, are potentially well suited for analyses of brain imaging data. A multivariate method (e.g. partial least squares) is a computational strategy that determines time-varying distributed patterns of the brain (as a function of a cognitive task). Compared to its univariate counterparts, a multivariate approach provides greater levels of sensitivity and reflects cooperative interactions among brain regions. Thus, by considering information across more than one measuring point, additional information on brain function can be revealed. Similarly, by considering information across more than one measuring technique, the nature of underlying cognitive processes become well-understood. Cognitive processes have been investigated in conjunction with multiple neuroimaging modalities (e.g. fMRI, sMRI, EEG, DTI), whereas the typical method has been to analyze each modality separately. Accordingly, little work has been carried out to examine the relation between different modalities. Indeed, due to the interconnected nature of brain processing, it is plausible that changes in one modality locally or distally modulate changes in another modality. This thesis focuses on multivariate and multimodal methods of image analysis applied to
Directory of Open Access Journals (Sweden)
Alecia J Carter
Full Text Available Animal personality, repeatable behaviour through time and across contexts, is ecologically and evolutionarily important as it can account for the exhibition of sub-optimal behaviours. Interspecific comparisons have been suggested as important for understanding the evolution of animal personality; however, these are seldom accomplished due, in part, to the lack of statistical tools for quantifying differences and similarities in behaviour between groups of individuals. We used nine species of closely-related coral reef fishes to investigate the usefulness of ecological community analyses for the analysis of between-species behavioural differences and behavioural heterogeneity. We first documented behavioural carryover across species by observing the fishes' behaviour and measuring their response to a threatening stimulus to quantify boldness. Bold fish spent more time away from the reef and fed more than shy fish. We then used ecological community analysis tools (canonical variate analysis, multi-response permutation procedure, and permutational analysis of multivariate dispersion and identified four 'clusters' of behaviourally similar fishes, and found that the species differ in the behavioural variation expressed; some species are more behaviourally heterogeneous than others. We found that ecological community analysis tools are easily and fruitfully applied to comparative studies of personality and encourage their use by future studies.
Directory of Open Access Journals (Sweden)
Mostafa Nejadhadad
2017-11-01
Full Text Available A geochemical exploration program was applied to recognize the anomalous geochemical haloes at the Ravanj lead mine, Delijan, Iran. Sampling of unweathered rocks were undertaken across rock exposures on a 10 × 10 meter grid (n = 302 as well as the accessible parts of underground mine A (n = 42. First, the threshold values of all elements were determined using the cut-off values used in the exploratory data analysis (EDA method. Then, for further studies, elements with lognormal distributions (Pb, Zn, Ag, As, Cd, Co, Cu, Sb, S, Sr, Th, Ba, Bi, Fe, Ni and Mn were selected. Robustness against outliers is achieved by application of central log ratio transformation to address the closure problems with compositional data prior to principle components analysis (PCA. Results of these analyses show that, in the Ravanj deposit, Pb mineralization is characterized by a Pb-Ba-Ag-Sb ± Zn ± Cd association. The supra-mineralization haloes are characterized by barite and tetrahedrite in a Ba- Th- Ag- Cu- Sb- As- Sr association and sub-mineralization haloes are comprised of pyrite and tetrahedrite, probably reflecting a Fe-Cu-As-Bi-Ni-Co-Mo-Mn association. Using univariate and multivariate geostatistical analyses (e.g., EDA and robust PCA, four anomalies were detected and mapped in Block A of the Ravanj deposit. Anomalies 1 and 2 are around the ancient orebodies. Anomaly 3 is located in a thin bedded limestone-shale intercalation unit that does not show significant mineralization. Drilling of the fourth anomaly suggested a low grade, non-economic Pb mineralization.
Assessment of Surface Water Quality in the Malaysian Coastal Waters by Using Multivariate Analyses
International Nuclear Information System (INIS)
Yap, C.K.; Chee, M.W.; Shamarina, S.; Edward, F.B.; Chew, W.; Tan, S.G.
2011-01-01
Coastal water samples were collected from 20 sampling sites in the southern part of Peninsular Malaysia. Seven physico-chemical parameters were measured directly in-situ while water samples were collected and analysed for 6 dissolved trace metal concentrations. The surface water (0-20 cm) physico-chemical parameters including temperature, salinity, dissolved oxygen (DO), pH, total dissolved solids (TDS), specific conductance (SpC) and turbidity while the dissolved trace metals were Cd, Cu, Fe, Ni, Pb and Zn. The ranges for the physico-chemical parameters were 28.07-35.6 degree Celsius for temperature, 0.18-32.42 ppt for salinity, 2.20-12.03 mg/ L for DO, 5.50-8.53 for pH, 0.24-31.65 mg/ L for TDS, 368-49452 μS/ cm for SpC and 0-262 NTU for turbidity while the dissolved metals (mg/ L) were 0.013-0.147 for Cd, 0.024-0.143 for Cu, 0.266-2.873 for Fe, 0.027-0.651 for Ni, 0.018-0.377 for Pb and 0.032-0.099 for Zn. Based on multivariate analysis (including correlation, cluster and principal component analyses), the polluted sites were found at Kg. Pasir Puteh and Tg. Kupang while Ni and Pb were identified as two major dissolved metals of high variation in the coastal waters. Therefore, water quality monitoring and control of release of untreated anthropogenic wastes into rivers and coastal waters are strongly needed. (author)
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
Kromhout, D.
2009-01-01
Within-person variability in measured values of multiple risk factors can bias their associations with disease. The multivariate regression calibration (RC) approach can correct for such measurement error and has been applied to studies in which true values or independent repeat measurements of the
DEFF Research Database (Denmark)
Tybjærg-Hansen, Anne
2009-01-01
Within-person variability in measured values of multiple risk factors can bias their associations with disease. The multivariate regression calibration (RC) approach can correct for such measurement error and has been applied to studies in which true values or independent repeat measurements...... of the risk factors are observed on a subsample. We extend the multivariate RC techniques to a meta-analysis framework where multiple studies provide independent repeat measurements and information on disease outcome. We consider the cases where some or all studies have repeat measurements, and compare study......-specific, averaged and empirical Bayes estimates of RC parameters. Additionally, we allow for binary covariates (e.g. smoking status) and for uncertainty and time trends in the measurement error corrections. Our methods are illustrated using a subset of individual participant data from prospective long-term studies...
Wong, Ka H; Razmovski-Naumovski, Valentina; Li, Kong M; Li, George Q; Chan, Kelvin
2014-07-01
Puerariae Lobatae Radix (PLR), the root of Pueraria lobata, is a traditional Chinese medicine for treating diabetes and cardiovascular diseases. Puerariae Thomsonii Radix (PTR), the root of Pueraria thomsonii, is a closely related species to PLR and has been used as a PLR substitute in clinical practice. The aim of this study was to compare the classification accuracy of high performance thin-layer chromatography (HPTLC) with that of ultra-performance liquid chromatography (UPLC) in differentiating PLR from PTR. The Matlab functions were used to facilitate the digitalisation and pre-processing of the HPTLC plates. Seven multivariate classification methods were evaluated for the two chromatographic methods. The results demonstrated that the HPTLC classification models were comparable to the UPLC classification models. In particular, k-nearest neighbours, partial least square-discriminant analysis, principal component analysis-discriminant analysis and support vector machine-discriminant analysis showed the highest rate of correct species classification, whilst the lowest classification rate was obtained from soft independent modelling of class analogy. In conclusion, HPTLC combined with multivariate analysis is a promising technique for the quality control and differentiation of PLR and PTR. Copyright © 2014 Elsevier B.V. All rights reserved.
A Multivariate Approach to Dilepton Analyses in the Upgraded ALICE Detector at CERN-LHC
AUTHOR|(CDS)2242451; Weber, Michael
ALICE, the dedicated heavy-ion experiment at CERN-LHC, will undergo a major upgrade in 2019/20. This work aims to assess the feasibility of conventional and multivariate analysis techniques for low-mass dielectron measurements in Pb-Pb collisions in a scenario involving the upgraded ALICE detector with a low magnetic field ($B=0.2~\\text{T}$). These electron-positron pairs are promising probes for the hot and dense medium, which is created in collisions of ultra-relativistic heavy nuclei, as they traverse the medium without significant final-state modifications. Due to their small signal-to-background ratio, high-purity dielectron samples are required. They can be provided by conventional analysis methods, which are based on sequential cuts, however at the price of low signal efficiency. This work shows that existing methods can be improved by employing multivariate approaches to reject different background sources of the dielectron invariant mass spectrum. The major background components are dielectrons from ...
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...
Ouchene-Khelifi, Nadjet-Amina; Ouchene, Nassim; Maftah, Abderrahman; Da Silva, Anne Blondeau; Lafri, Mohamed
2015-10-01
In Algeria, goat research has been largely neglected, in spite of the economic importance of this domestic species for rural livelihoods. Goat farming is traditional and cross-breeding practices are current. The phenotypic variability of the four main native breeds (Arabia, Makatia, M'zabite and Kabyle), and of two exotic breeds (Alpine and Saanen), was investigated for the first time, using multivariate discriminant analysis. A total of 892 females were sampled in a large area, including the cradle of the native breeds, and phenotyped with 23 quantitative measures and 10 qualitative traits. Our results suggested that cross-breeding practices have ever led to critical consequences, particularly for Makatia and M'zabite. The information reported in this study has to be carefully considered in order to establish governmental plan able to prevent the genetic dilution of the Algerian goat livestock.
Directory of Open Access Journals (Sweden)
Derek W Larson
Full Text Available Isolated small theropod teeth are abundant in vertebrate microfossil assemblages, and are frequently used in studies of species diversity in ancient ecosystems. However, determining the taxonomic affinities of these teeth is problematic due to an absence of associated diagnostic skeletal material. Species such as Dromaeosaurus albertensis, Richardoestesia gilmorei, and Saurornitholestes langstoni are known from skeletal remains that have been recovered exclusively from the Dinosaur Park Formation (Campanian. It is therefore likely that teeth from different formations widely disparate in age or geographic position are not referable to these species. Tooth taxa without any associated skeletal material, such as Paronychodon lacustris and Richardoestesia isosceles, have also been identified from multiple localities of disparate ages throughout the Late Cretaceous. To address this problem, a dataset of measurements of 1183 small theropod teeth (the most specimen-rich theropod tooth dataset ever constructed from North America ranging in age from Santonian through Maastrichtian were analyzed using multivariate statistical methods: canonical variate analysis, pairwise discriminant function analysis, and multivariate analysis of variance. The results indicate that teeth referred to the same taxon from different formations are often quantitatively distinct. In contrast, isolated teeth found in time equivalent formations are not quantitatively distinguishable from each other. These results support the hypothesis that small theropod taxa, like other dinosaurs in the Late Cretaceous, tend to be exclusive to discrete host formations. The methods outlined have great potential for future studies of isolated teeth worldwide, and may be the most useful non-destructive technique known of extracting the most data possible from isolated and fragmentary specimens. The ability to accurately assess species diversity and turnover through time based on isolated teeth
Larson, Derek W.; Currie, Philip J.
2013-01-01
Isolated small theropod teeth are abundant in vertebrate microfossil assemblages, and are frequently used in studies of species diversity in ancient ecosystems. However, determining the taxonomic affinities of these teeth is problematic due to an absence of associated diagnostic skeletal material. Species such as Dromaeosaurus albertensis, Richardoestesia gilmorei, and Saurornitholestes langstoni are known from skeletal remains that have been recovered exclusively from the Dinosaur Park Formation (Campanian). It is therefore likely that teeth from different formations widely disparate in age or geographic position are not referable to these species. Tooth taxa without any associated skeletal material, such as Paronychodon lacustris and Richardoestesia isosceles, have also been identified from multiple localities of disparate ages throughout the Late Cretaceous. To address this problem, a dataset of measurements of 1183 small theropod teeth (the most specimen-rich theropod tooth dataset ever constructed) from North America ranging in age from Santonian through Maastrichtian were analyzed using multivariate statistical methods: canonical variate analysis, pairwise discriminant function analysis, and multivariate analysis of variance. The results indicate that teeth referred to the same taxon from different formations are often quantitatively distinct. In contrast, isolated teeth found in time equivalent formations are not quantitatively distinguishable from each other. These results support the hypothesis that small theropod taxa, like other dinosaurs in the Late Cretaceous, tend to be exclusive to discrete host formations. The methods outlined have great potential for future studies of isolated teeth worldwide, and may be the most useful non-destructive technique known of extracting the most data possible from isolated and fragmentary specimens. The ability to accurately assess species diversity and turnover through time based on isolated teeth will help illuminate
Narrow band quantitative and multivariate electroencephalogram analysis of peri-adolescent period.
Martinez, E I Rodríguez; Barriga-Paulino, C I; Zapata, M I; Chinchilla, C; López-Jiménez, A M; Gómez, C M
2012-08-24
The peri-adolescent period is a crucial developmental moment of transition from childhood to emergent adulthood. The present report analyses the differences in Power Spectrum (PS) of the Electroencephalogram (EEG) between late childhood (24 children between 8 and 13 years old) and young adulthood (24 young adults between 18 and 23 years old). The narrow band analysis of the Electroencephalogram was computed in the frequency range of 0-20 Hz. The analysis of mean and variance suggested that six frequency ranges presented a different rate of maturation at these ages, namely: low delta, delta-theta, low alpha, high alpha, low beta and high beta. For most of these bands the maturation seems to occur later in anterior sites than posterior sites. Correlational analysis showed a lower pattern of correlation between different frequencies in children than in young adults, suggesting a certain asynchrony in the maturation of different rhythms. The topographical analysis revealed similar topographies of the different rhythms in children and young adults. Principal Component Analysis (PCA) demonstrated the same internal structure for the Electroencephalogram of both age groups. Principal Component Analysis allowed to separate four subcomponents in the alpha range. All these subcomponents peaked at a lower frequency in children than in young adults. The present approaches complement and solve some of the incertitudes when the classical brain broad rhythm analysis is applied. Children have a higher absolute power than young adults for frequency ranges between 0-20 Hz, the correlation of Power Spectrum (PS) with age and the variance age comparison showed that there are six ranges of frequencies that can distinguish the level of EEG maturation in children and adults. The establishment of maturational order of different frequencies and its possible maturational interdependence would require a complete series including all the different ages.
Nonparametric Analyses of Log-Periodic Precursors to Financial Crashes
Zhou, Wei-Xing; Sornette, Didier
We apply two nonparametric methods to further test the hypothesis that log-periodicity characterizes the detrended price trajectory of large financial indices prior to financial crashes or strong corrections. The term "parametric" refers here to the use of the log-periodic power law formula to fit the data; in contrast, "nonparametric" refers to the use of general tools such as Fourier transform, and in the present case the Hilbert transform and the so-called (H, q)-analysis. The analysis using the (H, q)-derivative is applied to seven time series ending with the October 1987 crash, the October 1997 correction and the April 2000 crash of the Dow Jones Industrial Average (DJIA), the Standard & Poor 500 and Nasdaq indices. The Hilbert transform is applied to two detrended price time series in terms of the ln(tc-t) variable, where tc is the time of the crash. Taking all results together, we find strong evidence for a universal fundamental log-frequency f=1.02±0.05 corresponding to the scaling ratio λ=2.67±0.12. These values are in very good agreement with those obtained in earlier works with different parametric techniques. This note is extracted from a long unpublished report with 58 figures available at , which extensively describes the evidence we have accumulated on these seven time series, in particular by presenting all relevant details so that the reader can judge for himself or herself the validity and robustness of the results.
Short-Period Binary Stars: Observations, Analyses, and Results
Milone, Eugene F; Hobill, David W
2008-01-01
Short-period binaries run the gamut from widely separated stars to black-hole pairs; in between are systems that include neutron stars and white dwarfs, and partially evolved systems such as tidally distorted and over-contact systems. These objects represent stages of evolution of binary stars, and their degrees of separation provide critical clues to how their evolutionary paths differ from that of single stars. The widest and least distorted systems provide astronomers with the essential precise data needed to study all stars: mass and radius. The interactions of binary star components, on the other hand, provide a natural laboratory to observe how the matter in these stars behaves under different and often varying physical conditions. Thus, cataclysmic variables with and without overpoweringly strong magnetic fields, and stars with densities from that found in the Sun to the degenerate matter of white dwarfs and the ultra-compact states of neutron stars and black holes are all discussed. The extensive inde...
NPP Krsko periodic safety review. Safety assessment and analyses
International Nuclear Information System (INIS)
Basic, I.; Spiler, J.; Thaulez, F.
2002-01-01
Definition of a PSR (Periodic Safety Review) project is a comprehensive safety review of a plant after ten years of operation. The objective is a verification by means of a comprehensive review using current methods that the plant remains safe when judged against current safety objectives and practices and that adequate arrangements are in place to maintain plant safety. The overall goals of the NEK PSR Program are defined in compliance with the basic role of a PSR and the current practice typical for most of the countries in EU. This practice is described in the related guides and good practice documents issued by international organizations. The overall goals of the NEK PSR are formulated as follows: to demonstrate that the plant is as safe as originally intended; to evaluate the actual plant status with respect to aging and wear-out identifying any structures, systems or components that could limit the life of the plant in the foreseeable future, and to identify appropriate corrective actions, where needed; to compare current level of safety in the light of modern standards and knowledge, and to identify where improvements would be beneficial for minimizing deviations at justifiable costs. The Krsko PSR will address the following safety factors: Operational Experience, Safety Assessment, EQ and Aging Management, Safety Culture, Emergency Planning, Environmental Impact and Radioactive Waste.(author)
Malkoc, S; Yazici, B
2017-02-01
A total of 50 surface industrial area soil in Eskisehir, Turkey were collected and the concentrations of As, Cr, Cd, Co, Cu, Ni, Pb, Zn, Fe and Mg, at 11.34, 95.8, 1.37, 15.28, 33.06, 143.65, 14.34, 78.79 mg/kg, 188.80% and 78.70%, respectively. The EF values for As, Cu, Pb and Zn at a number of sampling sites were found to be the highest among metals. Igeo-index results show that the study area is moderately polluted with respect to As, Cd, Ni. According to guideline values of Turkey Environmental Quality Standard for Soils, there is no problem for Pb, but the Cd values are fairly high. However, Cr, Cu, Ni and Zn values mostly exceed the limits. Cluster analyses suggested that soil the contaminator values are homogenous in those sub classes. The prevention and remediation of the heavy metal soil pollution should focus on these high-risk areas in the future.
Masoud, Alaa A.; El-Horiny, Mohamed M.; Atwia, Mohamed G.; Gemail, Khaled S.; Koike, Katsuaki
2018-06-01
Salinization of groundwater and soil resources has long been a serious environmental hazard in arid regions. This study was conducted to investigate and document the factors controlling such salinization and their inter-relationships in the Dakhla Oasis (Egypt). To accomplish this, 60 groundwater samples and 31 soil samples were collected in February 2014. Factor analysis (FA) and hierarchical cluster analysis (HCA) were integrated with geostatistical analyses to characterize the chemical properties of groundwater and soil and their spatial patterns, identify the factors controlling the pattern variability, and clarify the salinization mechanism. Groundwater quality standards revealed emergence of salinization (av. 885.8 mg/L) and extreme occurrences of Fe2+ (av. 17.22 mg/L) and Mn2+ (av. 2.38 mg/L). Soils were highly salt-affected (av. 15.2 dS m-1) and slightly alkaline (av. pH = 7.7). Evaporation and ion-exchange processes governed the evolution of two main water types: Na-Cl (52%) and Ca-Mg-Cl (47%), respectively. Salinization leads the chemical variability of both resources. Distinctive patterns of slight salinization marked the northern part and intense salinization marked the middle and southern parts. Congruence in the resources clusters confirmed common geology, soil types, and urban and agricultural practices. Minimizing the environmental and socioeconomic impacts of the resources salinization urges the need for better understanding of the hydrochemical characteristics and prediction of quality changes.
Directory of Open Access Journals (Sweden)
Aeberli D
1999-01-01
Full Text Available Das primäre Ziel dieser retrospektiven Studie war die Evaluation von routinemäßig anwendbaren Kriterien, welche die Fragmentierung von Nierensteinen unter extrakorporaler Stoßwellenlithotripsie (ESWL voraussagen können. Die Serie umfaßte 202 konsekutive Patienten (121 Männer, 81 Frauen mit einem medianen Alter von 48 Jahren (19-81 Jahre, die an der Urologischen Universitätsklinik Bern mit dem originalen Dornier HM-3 behandelt wurden. Einschlußkriterien waren Einzelsteine von 10-30 mm Größe mit Lage im Nierenbeckenkelchsystem. Die Steine wurden im Abdomen-Leerbild nach ihrer Lage, Größe, Form, Dichte (im Vergleich zur 12. Rippe, Struktur und Oberfläche beurteilt. Weiter wurden Alter, Geschlecht und Body-Mass-Index des Patienten berücksichtigt. Die Desintegration wurde am 1. Tag nach ESWL mittels Abdomen-Leerbild dokumentiert. Zielwert der Analyse war die binäre Variable steinfrei versus Restfragmente, gegen die alle präoperativen Parameter mittels multivariater logistischer Regression getestet wurden. Die Erfolgsrate betrug 95,5 %; 42 Patienten (20,8 % waren steinfrei und 151 Patienten (74,7 % hatten sog. klinisch insignifikante Restfragmente (5 mm oder kleiner. Von den Männern waren 14,9 %, von den Frauen 29,6 % steinfrei (p = 0,01. Alle anderen Parameter erreichten keine statistische Signifikanz, möglicherweise aufgrund zu kleiner Subgruppen (fehlende statistische Macht. Die Desintegrationsrate des HM-3 für Nierensteine ist insgesamt ausgezeichnet, bei Frauen noch besser als bei Männern. Gerade wegen dieser hohen Erfolgsrate wären größere Patientenzahlen notwendig, um eventuelle Unterschiede zwischen den präinterventionellen Parametern definieren zu können.
Singh, Jitendra; Hari, Vittal; Sharma, Tarul; Karmakar, Subhankar; Ghosh, Subimal
2016-04-01
The statistical assumption of stationarity in hydrologic extreme time/event series has been relied heavily in frequency analysis. However, due to the analytically perceivable impacts of climate change, urbanization and concomitant land use pattern, assumption of stationarity in hydrologic time series will draw erroneous results, which in turn may affect the policy and decision-making. Past studies provided sufficient evidences on changes in the characteristics of Indian monsoon precipitation extremes and further it has been attributed to climate change and urbanization, which shows need of nonstationary analysis on the Indian monsoon extremes. Therefore, a comprehensive multivariate nonstationary frequency analysis has been conducted for the entire India to identify the precipitation characteristics (intensity, duration and depth) responsible for significant nonstationarity in the Indian monsoon. We use 1o resolution of precipitation data for a period of 1901-2004, in a Generalized Additive Model for Location, Scale and Shape (GAMLSS) framework. A cluster of GAMLSS models has been developed by considering nonstationarity in different combinations of distribution parameters through different regression techniques, and the best-fit model is further applied for bivariate analysis. A population density data has been utilized to identify the urban, urbanizing and rural regions. The results showed significant differences in the stationary and nonstationary bivariate return periods for the urbanizing grids, when compared to urbanized and rural grids. A comprehensive multivariate analysis has also been conducted to identify the precipitation characteristics particularly responsible for imprinting signature of nonstationarity.
Appolloni, L; Sandulli, R; Vetrano, G; Russo, G F
2018-05-15
Marine Protected Areas are considered key tools for conservation of coastal ecosystems. However, many reserves are characterized by several problems mainly related to inadequate zonings that often do not protect high biodiversity and propagule supply areas precluding, at the same time, economic important zones for local interests. The Gulf of Naples is here employed as a study area to assess the effects of inclusion of different conservation features and costs in reserve design process. In particular eight scenarios are developed using graph theory to identify propagule source patches and fishing and exploitation activities as costs-in-use for local population. Scenarios elaborated by MARXAN, software commonly used for marine conservation planning, are compared using multivariate analyses (MDS, PERMANOVA and PERMDISP) in order to assess input data having greatest effects on protected areas selection. MARXAN is heuristic software able to give a number of different correct results, all of them near to the best solution. Its outputs show that the most important areas to be protected, in order to ensure long-term habitat life and adequate propagule supply, are mainly located around the Gulf islands. In addition through statistical analyses it allowed us to prove that different choices on conservation features lead to statistically different scenarios. The presence of propagule supply patches forces MARXAN to select almost the same areas to protect decreasingly different MARXAN results and, thus, choices for reserves area selection. The multivariate analyses applied here to marine spatial planning proved to be very helpful allowing to identify i) how different scenario input data affect MARXAN and ii) what features have to be taken into account in study areas characterized by peculiar biological and economic interests. Copyright © 2018 Elsevier Ltd. All rights reserved.
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.
International Nuclear Information System (INIS)
Zhang Chaosheng
2006-01-01
Galway is a small but rapidly growing tourism city in western Ireland. To evaluate its environmental quality, a total of 166 surface soil samples (0-10 cm depth) were collected from parks and grasslands at the density of 1 sample per 0.25 km 2 at the end of 2004. All samples were analysed using ICP-AES for the near-total concentrations of 26 chemical elements. Multivariate statistics and GIS techniques were applied to classify the elements and to identify elements influenced by human activities. Cluster analysis (Canada) and principal component analysis (PCA) classified the elements into two groups: the first group predominantly derived from natural sources, the second being influenced by human activities. GIS mapping is a powerful tool in identifying the possible sources of pollutants. Relatively high concentrations of Cu, Pb and Zn were found in the city centre, old residential areas, and along major traffic routes, showing significant effects of traffic pollution. The element As is enriched in soils of the old built-up areas, which can be attributed to coal and peat combustion for home heating. Such significant spatial patterns of pollutants displayed by urban soils may imply potential health threat to residents of the contaminated areas of the city. - Multivariate statistics and GIS are useful tools to identify pollutants in urban soils
Roldan-Valadez, Ernesto; Suarez-May, Marcela A; Favila, Rafael; Aguilar-Castañeda, Erika; Rios, Camilo
2015-07-01
Interest in the lateralization of the human brain is evident through a multidisciplinary number of scientific studies. Understanding volumetric brain asymmetries allows the distinction between normal development stages and behavior, as well as brain diseases. We aimed to evaluate volumetric asymmetries in order to select the best gyri able to classify right- versus left cerebral hemispheres. A cross-sectional study performed in 47 right-handed young-adults healthy volunteers. SPM-based software performed brain segmentation, automatic labeling and volumetric analyses for 54 regions involving the cerebral lobes, basal ganglia and cerebellum from each cerebral hemisphere. Multivariate discriminant analysis (DA) allowed the assembling of a predictive model. DA revealed one discriminant function that significantly differentiated left vs. right cerebral hemispheres: Wilks' λ = 0.008, χ(2) (9) = 238.837, P brain gyri are able to accurately classify left vs. right cerebral hemispheres by using a multivariate approach; the selected regions correspond to key brain areas involved in attention, internal thought, vision and language; our findings favored the concept that lateralization has been evolutionary favored by mental processes increasing cognitive efficiency and brain capacity. © 2015 Wiley Periodicals, Inc.
Cavalcante, Y L; Hauser-Davis, R A; Saraiva, A C F; Brandão, I L S; Oliveira, T F; Silveira, A M
2013-01-01
This paper compared and evaluated seasonal variations in physico-chemical parameters and metals at a hydroelectric power station reservoir by applying Multivariate Analyses and Artificial Neural Networks (ANN) statistical techniques. A Factor Analysis was used to reduce the number of variables: the first factor was composed of elements Ca, K, Mg and Na, and the second by Chemical Oxygen Demand. The ANN showed 100% correct classifications in training and validation samples. Physico-chemical analyses showed that water pH values were not statistically different between the dry and rainy seasons, while temperature, conductivity, alkalinity, ammonia and DO were higher in the dry period. TSS, hardness and COD, on the other hand, were higher during the rainy season. The statistical analyses showed that Ca, K, Mg and Na are directly connected to the Chemical Oxygen Demand, which indicates a possibility of their input into the reservoir system by domestic sewage and agricultural run-offs. These statistical applications, thus, are also relevant in cases of environmental management and policy decision-making processes, to identify which factors should be further studied and/or modified to recover degraded or contaminated water bodies. Copyright © 2012 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Hasmukh J Prajapati
Full Text Available To develop the treatment algorithm from multivariate survival analyses (MVA in patients with Barcelona clinic liver cancer (BCLC C (advanced Hepatocellular carcinoma (HCC patients treated with Trans-arterial Chemoembolization (TACE.Consecutive unresectable and non-tranplantable patients with advanced HCC, who received DEB TACE were studied. A total of 238 patients (mean age, 62.4yrs was included in the study. Survivals were analyzed according to different parameters from the time of the 1st DEB TACE. Kaplan Meier and Cox Proportional Hazard model were used for survival analysis. The SS was constructed from MVA and named BCLC C HCC Prognostic (BCHP staging system (SS.Overall median survival (OS was 16.2 months. In HCC patients with venous thrombosis (VT of large vein [main portal vein (PV, right or left PV, hepatic vein, inferior vena cava] (22.7% versus small vein (segmental/subsegmental PV (9.7% versus no VT had OSs of 6.4 months versus 20 months versus 22.8 months respectively (p<0.001. On MVA, the significant independent prognostic factors (PFs of survival were CP class, eastern cooperative oncology group (ECOG performance status (PS, single HCC<5 cm, site of VT, metastases, serum creatinine and serum alpha-feto protein. Based on these PFs, the BCHP staging system was constructed. The OSs of stages I, II and III were 28.4 months, 11.8 months and 2.4 months accordingly (p<0.001. The treatment plan was proposed according to the different stages.On MVA of patients with advanced HCC treated with TACE, significant independent prognostic factors (PFs of survival were CP class, ECOG PS, single HCC<5 cm or others, site of VT, metastases, serum creatinine and serum alpha-feto protein. New BCHP SS was proposed based on MVA data to identify the suitable advanced HCC patients for TACE treatments.
Energy Technology Data Exchange (ETDEWEB)
Farahati, J.; Schulz, G.; Koerber, C.; Geling, M.; Schmeider, P.; Reiners, Chr. [Wuerzburg Univ. (Germany). Klinik fuer Nuklearmedizin; Wendler, J. [Erlangen-Nuernberg Univ. (Germany). Klinik fuer Innere Medizin III; Kenn, W. [Wuerzburg Univ. (Germany). Inst. fuer Roentgendiagnostik; Reidemeister, C. [Wuerzburg Univ. (Germany). Klinik fuer Innere Medizin
2002-04-01
Objective: In this prospective study, the time to remission after radiosynovectomy (RSV) was analyzed and the influence of age, sex, underlying disease, type of joint, and duration of illness on the success rate of RSV was determined. Methods: A total number of 57 patients with rheumatoid arthritis (n = 33) and arthrosis (n = 21) with a total number of 130 treated joints (36 knee, 66 small and 28 medium-size joints) were monitored using visual analogue scales (VAS) from one week before RSV up to four to six months after RSV. The patients had to answer 3 times daily for pain intensity of the treated joint. The time until remission was determined according to the Kaplan-Meier survivorship function. The influence of the prognosis parameters on outcome of RSV was determined by multivariate discriminant analysis. Results: After six months, the probability of pain relief of more than 20% amounted to 78% and was significantly dependent on the age of the patient (p = 0.02) and the duration of illness (p = 0.05), however not on sex (p = 0.17), underlying disease (p = 0.23), and type of joint (p = 0.69). Conclusion: Irrespective of sex, type of joint and underlying disease, a measurable pain relief can be achieved with RSV in 78% of the patients with synovitis, whereby effectiveness is decreasing with increasing age and progress of illness. (orig.) [German] Ziel: In dieser prospektiven Studie wurde die Zeit bis zur Remission nach einer Radiosynoviorthese (RSO) untersucht. Ebenso wurde der Einfluss von Alter, Geschlecht, Grunderkrankung, Gelenktyp und Erkrankungsdauer auf die Erfolgsrate der RSO ermittelt. Methodik: Bei insgesamt 57 Patienten mit rheumatoider Arthritis (n = 33) und Arthritis bei aktivierter Arthrose (n = 24) wurden 130 Gelenke (36 Kniegelenke, 66 kleine und 28 mittelgrosse Gelenke) behandelt. Die Patienten wurden unter Verwendung so genannter visueller Analogskalen eine Woche vor RSO und vier bis sechs Monate danach 3-mal taeglich zur Schmerzintensitaet des
International Nuclear Information System (INIS)
Renz, Manuel
2008-01-01
half. In the second part of this diploma thesis, a method for the combination of three multivariate single-top analyses using an integrated luminosity of 2.2 fb -1 is presented. For this purpose the discriminants of the Likelihood Function analysis, the Matrix Element method and the Neural Network analysis are used as input variables to a neural network. Overall four different networks are trained, one for events with two or three jets and one or two SecVtx tags, respectively. Using a binned likelihood function, the outputs of these networks are fitted to the output distribution of observed events. A single top-quark production cross section of σ single-top = 2.2 -0.7 +0.8 pb is measured. Ensemble tests are performed for the calculation of the sensitivity and observed significance, which are found to be 4.8σ and 3.9σ, respectively. Hence the improvement of this combination is roughly 8% in comparison with sensitivities found by the individual analyses. Due to the proportionality of σ single-top and |V tb | 2 and under the assumption V tb >> V ts , V td , a value for |V tb | is quoted: |V tb | = 0.88 -0.12 +0.14 (exp.) ± 0.07(theo.). It can be seen, that the given uncertainties are too large for a verification or falsification of the unitarity assumption of the CKM-matrix. Parallel to this combination a further combination method (NEAT-combination) has been developed. This combination uses a neural network trained with a neuroevolution technique, which optimizes the neural network architecture and weights through the use of genetic algorithms. In this analysis an improvement of roughly 12% could be reached. In figure 7.1 the current situation for the measurement of the single top-quark production cross section is summarized. After collecting more data, CDF will be able to observe single top-quark production with a significance larger than 5.0σ. Nevertheless, the cross section measurement will still have large uncertainties on the level of 20%. Precise measurements
Energy Technology Data Exchange (ETDEWEB)
Renz, Manuel; /Karlsruhe U., EKP
2008-06-01
into half. In the second part of this diploma thesis, a method for the combination of three multivariate single-top analyses using an integrated luminosity of 2.2 fb{sup -1} is presented. For this purpose the discriminants of the Likelihood Function analysis, the Matrix Element method and the Neural Network analysis are used as input variables to a neural network. Overall four different networks are trained, one for events with two or three jets and one or two SecVtx tags, respectively. Using a binned likelihood function, the outputs of these networks are fitted to the output distribution of observed events. A single top-quark production cross section of {sigma}{sub single-top} = 2.2{sub -0.7}{sup +0.8} pb is measured. Ensemble tests are performed for the calculation of the sensitivity and observed significance, which are found to be 4.8{sigma} and 3.9{sigma}, respectively. Hence the improvement of this combination is roughly 8% in comparison with sensitivities found by the individual analyses. Due to the proportionality of {sigma}{sub single-top} and |V{sub tb}|{sup 2} and under the assumption V{sub tb} >> V{sub ts}, V{sub td}, a value for |V{sub tb}| is quoted: |V{sub tb}| = 0.88{sub -0.12}{sup +0.14}(exp.) {+-} 0.07(theo.). It can be seen, that the given uncertainties are too large for a verification or falsification of the unitarity assumption of the CKM-matrix. Parallel to this combination a further combination method (NEAT-combination) has been developed. This combination uses a neural network trained with a neuroevolution technique, which optimizes the neural network architecture and weights through the use of genetic algorithms. In this analysis an improvement of roughly 12% could be reached. In figure 7.1 the current situation for the measurement of the single top-quark production cross section is summarized. After collecting more data, CDF will be able to observe single top-quark production with a significance larger than 5.0{sigma}. Nevertheless, the cross
Wilson, Robbie S; James, Rob S; David, Gwendolyn; Hermann, Ecki; Morgan, Oliver J; Niehaus, Amanda C; Hunter, Andrew; Thake, Doug; Smith, Michelle D
2016-11-01
The development of a comprehensive protocol for quantifying soccer-specific skill could markedly improve both talent identification and development. Surprisingly, most protocols for talent identification in soccer still focus on the more generic athletic attributes of team sports, such as speed, strength, agility and endurance, rather than on a player's technical skills. We used a multivariate methodology borrowed from evolutionary analyses of adaptation to develop our quantitative assessment of individual soccer-specific skill. We tested the performance of 40 individual academy-level players in eight different soccer-specific tasks across an age range of 13-18 years old. We first quantified the repeatability of each skill performance then explored the effects of age on soccer-specific skill, correlations between each of the pairs of skill tasks independent of age, and finally developed an individual metric of overall skill performance that could be easily used by coaches. All of our measured traits were highly repeatable when assessed over a short period and we found that an individual's overall skill - as well as their performance in their best task - was strongly positively correlated with age. Most importantly, our study established a simple but comprehensive methodology for assessing skill performance in soccer players, thus allowing coaches to rapidly assess the relative abilities of their players, identify promising youths and work on eliminating skill deficits in players.
Guglielminotti, Jean; Dechartres, Agnès; Mentré, France; Montravers, Philippe; Longrois, Dan; Laouénan, Cedric
2015-10-01
Prognostic research studies in anesthesiology aim to identify risk factors for an outcome (explanatory studies) or calculate the risk of this outcome on the basis of patients' risk factors (predictive studies). Multivariable models express the relationship between predictors and an outcome and are used in both explanatory and predictive studies. Model development demands a strict methodology and a clear reporting to assess its reliability. In this methodological descriptive review, we critically assessed the reporting and methodology of multivariable analysis used in observational prognostic studies published in anesthesiology journals. A systematic search was conducted on Medline through Web of Knowledge, PubMed, and journal websites to identify observational prognostic studies with multivariable analysis published in Anesthesiology, Anesthesia & Analgesia, British Journal of Anaesthesia, and Anaesthesia in 2010 and 2011. Data were extracted by 2 independent readers. First, studies were analyzed with respect to reporting of outcomes, design, size, methods of analysis, model performance (discrimination and calibration), model validation, clinical usefulness, and STROBE (i.e., Strengthening the Reporting of Observational Studies in Epidemiology) checklist. A reporting rate was calculated on the basis of 21 items of the aforementioned points. Second, they were analyzed with respect to some predefined methodological points. Eighty-six studies were included: 87.2% were explanatory and 80.2% investigated a postoperative event. The reporting was fairly good, with a median reporting rate of 79% (75% in explanatory studies and 100% in predictive studies). Six items had a reporting rate website. Limiting the number of candidate variables, including cases with missing data, and not arbitrarily categorizing continuous variables should be encouraged.
Directory of Open Access Journals (Sweden)
Abdelfattah M. Selim
2018-03-01
Full Text Available Aim: The present cross-sectional study was conducted to determine the seroprevalence and potential risk factors associated with Bovine viral diarrhea virus (BVDV disease in cattle and buffaloes in Egypt, to model the potential risk factors associated with the disease using logistic regression (LR models, and to fit the best predictive model for the current data. Materials and Methods: A total of 740 blood samples were collected within November 2012-March 2013 from animals aged between 6 months and 3 years. The potential risk factors studied were species, age, sex, and herd location. All serum samples were examined with indirect ELIZA test for antibody detection. Data were analyzed with different statistical approaches such as Chi-square test, odds ratios (OR, univariable, and multivariable LR models. Results: Results revealed a non-significant association between being seropositive with BVDV and all risk factors, except for species of animal. Seroprevalence percentages were 40% and 23% for cattle and buffaloes, respectively. OR for all categories were close to one with the highest OR for cattle relative to buffaloes, which was 2.237. Likelihood ratio tests showed a significant drop of the -2LL from univariable LR to multivariable LR models. Conclusion: There was an evidence of high seroprevalence of BVDV among cattle as compared with buffaloes with the possibility of infection in different age groups of animals. In addition, multivariable LR model was proved to provide more information for association and prediction purposes relative to univariable LR models and Chi-square tests if we have more than one predictor.
Ma, Chunhui; Dastmalchi, Keyvan; Flores, Gema; Wu, Shi-Biao; Pedraza-Peñalosa, Paola; Long, Chunlin; Kennelly, Edward J
2013-04-10
There are many neotropical blueberries, and recent studies have shown that some have even stronger antioxidant activity than the well-known edible North American blueberries. Antioxidant marker compounds were predicted by applying multivariate statistics to data from LC-TOF-MS analysis and antioxidant assays of 3 North American blueberry species (Vaccinium corymbosum, Vaccinium angustifolium, and a defined mixture of Vaccinium virgatum with V. corymbosum) and 12 neotropical blueberry species (Anthopterus wardii, Cavendishia grandifolia, Cavendishia isernii, Ceratostema silvicola, Disterigma rimbachii, Macleania coccoloboides, Macleania cordifolia, Macleania rupestris, Satyria boliviana, Sphyrospermum buxifolium, Sphyrospermum cordifolium, and Sphyrospermum ellipticum). Fourteen antioxidant markers were detected, and 12 of these, including 7 anthocyanins, 3 flavonols, 1 hydroxycinnamic acid, and 1 iridoid glycoside, were identified. This application of multivariate analysis to bioactivity and mass data can be used for identification of pharmacologically active natural products and may help to determine which neotropical blueberry species will be prioritized for agricultural development. Also, the compositional differences between North American and neotropical blueberries were determined by chemometric analysis, and 44 marker compounds including 16 anthocyanins, 15 flavonoids, 7 hydroxycinnamic acid derivatives, 5 triterpene glycosides, and 1 iridoid glycoside were identified.
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.
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.
International Nuclear Information System (INIS)
Luca, Michele De; Ragno, Gaetano; Ioele, Giuseppina; Tauler, Romà
2014-01-01
Highlights: • A new MCR-ALS algorithm is proposed for the analysis of incomplete fused multiset. • Resolution of the data allowed the description of amiloride kinetic photodegradation. • The new MCR-ALS algorithm can be easily applied to other drugs and chemicals. - Abstract: An advanced and powerful chemometric approach is proposed for the analysis of incomplete multiset data obtained by fusion of hyphenated liquid chromatographic DAD/MS data with UV spectrophotometric data from acid–base titration and kinetic degradation experiments. Column- and row-wise augmented data blocks were combined and simultaneously processed by means of a new version of the multivariate curve resolution-alternating least squares (MCR-ALS) technique, including the simultaneous analysis of incomplete multiset data from different instrumental techniques. The proposed procedure was applied to the detailed study of the kinetic photodegradation process of the amiloride (AML) drug. All chemical species involved in the degradation and equilibrium reactions were resolved and the pH dependent kinetic pathway described
Energy Technology Data Exchange (ETDEWEB)
Luca, Michele De, E-mail: michele.deluca@unical.it [Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Via P. Bucci, Rende, CS 87036 (Italy); Ragno, Gaetano; Ioele, Giuseppina [Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Via P. Bucci, Rende, CS 87036 (Italy); Tauler, Romà [Department of Environmental Chemistry, IDAEA-CSIC, C/Jordi Girona, 18-26, Barcelona 08034 (Spain)
2014-07-21
Highlights: • A new MCR-ALS algorithm is proposed for the analysis of incomplete fused multiset. • Resolution of the data allowed the description of amiloride kinetic photodegradation. • The new MCR-ALS algorithm can be easily applied to other drugs and chemicals. - Abstract: An advanced and powerful chemometric approach is proposed for the analysis of incomplete multiset data obtained by fusion of hyphenated liquid chromatographic DAD/MS data with UV spectrophotometric data from acid–base titration and kinetic degradation experiments. Column- and row-wise augmented data blocks were combined and simultaneously processed by means of a new version of the multivariate curve resolution-alternating least squares (MCR-ALS) technique, including the simultaneous analysis of incomplete multiset data from different instrumental techniques. The proposed procedure was applied to the detailed study of the kinetic photodegradation process of the amiloride (AML) drug. All chemical species involved in the degradation and equilibrium reactions were resolved and the pH dependent kinetic pathway described.
Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque
2017-01-01
Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).
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.
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.
Habermann, Jörg M.; McHenry, Lindsay J.; Stollhofen, Harald; Tolosana-Delgado, Raimon; Stanistreet, Ian G.; Deino, Alan L.
2016-06-01
The chronology of Pleistocene flora and fauna, including hominin remains and associated Oldowan industries in Bed I, Olduvai Gorge, Tanzania, is primarily based on 40Ar/39Ar dating of intercalated tuffs and lavas, combined with detailed tephrostratigraphic correlations within the basin. Although a high-resolution chronostratigraphic framework has been established for the eastern part of the Olduvai Basin, the western subbasin is less well known due in part to major lateral facies changes within Bed I combined with discontinuous exposure. We address these correlation difficulties using the discriminative power of the chemical composition of the major juvenile mineral phases (augite, anorthoclase, plagioclase) from tuffs, volcaniclastic sandstones, siliciclastic units, and lavas. We statistically evaluate these compositions, obtained from electron probe micro-analysis, applying principal component analysis and discriminant analysis to develop discriminant models that successfully classify most Bed I volcanic units. The correlations, resulting from integrated analyses of all target minerals, provide a basin-wide Bed I chemostratigraphic framework at high lateral and vertical resolution, consistent with the known geological context, that expands and refines the geochemical databases currently available. Correlation of proximal ignimbrites at the First Fault with medial and distal Lower Bed I successions of the western basin enables assessment of lateral facies and thickness trends that confirm Ngorongoro Volcano as the primary source for Lower Bed I, whereas Upper Bed I sediment supply is mainly from Olmoti Volcano. Compositional similarity between Tuff IA, Bed I lava, and Mafic Tuffs II and III single-grain fingerprints, together with north- and northwestward thinning of Bed I lava, suggests a common Ngorongoro source for these units. The techniques applied herein improve upon previous work by evaluating compositional affinities with statistical rigor rather than
Levee reliability analyses for various flood return periods - a case study in southern Taiwan
Huang, W.-C.; Yu, H.-W.; Weng, M.-C.
2015-04-01
In recent years, heavy rainfall conditions have caused disasters around the world. To prevent losses by floods, levees have often been constructed in inundation-prone areas. This study performed reliability analyses for the Chiuliao First Levee in southern Taiwan. The failure-related parameters were the water level, the scouring depth, and the in situ friction angle. Three major failure mechanisms were considered: the slope sliding failure of the levee and the sliding and overturning failures of the retaining wall. When the variability of the in situ friction angle and the scouring depth are considered for various flood return periods, the variations of the factor of safety for the different failure mechanisms show that the retaining wall sliding and overturning failures are more sensitive to the change of the friction angle. When the flood return period is greater than 2 years, the levee could fail with slope sliding for all values of the water level difference. The results of levee stability analysis considering the variability of different parameters could aid engineers in designing the levee cross sections, especially with potential failure mechanisms in mind.
Faulkner, Danny R.; Samec, Ronald G.; Caton, Daniel B.
2018-06-01
We report here on a period study and the analysis of BVRcIc light curves (taken in 2017) of MT Cam (GSC03737-01085), which is a solar type (T ~ 5500K) eclipsing binary. D. Caton observed MT Cam on 05, 14, 15, 16, and 17, December 2017 with the 0.81-m reflector at Dark Sky Observatory. Six times of minimum light were calculated from four primary eclipses and two secondary eclipses:HJD I = 24 58092.4937±0.0002, 2458102.74600±0.0021, 2458104.5769±0.0002, 2458104.9434±0.0029HJD II = 2458103.6610±0.0001, 2458104.7607±0.0020,Six times of minimum light were also calculated from data taken by Terrell, Gross, and Cooney, in their 2016 and 2004 observations (reported in IBVS #6166; TGC, hereafter). In addition, six more times of minimum light were taken from the literature. From all 18 times of minimum light, we determined the following light elements:JD Hel Min I=2458102.7460(4) + 0.36613937(5) EWe found the orbital period was constant over the 14 years spanning all observations. We note that TGC found a slightly increasing period. However, our results were obtained from a period study rather than comparison of observations from only two epochs by the Wilson-Devinney (W-D) Program. A BVRcIc Johnson-Cousins filtered simultaneous W-D Program solution gives a mass ratio (0.3385±0.0014) very nearly the same as TGC’s (0.347±0.003), and a component temperature difference of only ~40 K. As with TGC, no spot was needed in the modeling. Our modeling (beginning with Binary Maker 3.0 fits) was done without prior knowledge of TGC’s. This shows the agreement achieved when independent analyses are done with the W-D code. The present observations were taken 1.8 years later than the last curves by TGC, so some variation is expected.The Roche Lobe fill-out of the binary is ~13% and the inclination is ~83.5 degrees. The system is a shallow contact W-type W UMa Binary, albeit, the amplitudes of the primary and secondary eclipse are very nearly identical. An eclipse duration of ~21
Energy Technology Data Exchange (ETDEWEB)
Ali, Kashif; Maltese, Federica [Leiden University, Division of Pharmacognosy, Section Metabolomics, Institute of Biology (Netherlands); Toepfer, Reinhard [Institute for Grapevine Breeding Geilweilerhof, Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants (Germany); Choi, Young Hae, E-mail: y.choi@chem.leidenuniv.nl; Verpoorte, Robert [Leiden University, Division of Pharmacognosy, Section Metabolomics, Institute of Biology (Netherlands)
2011-04-15
{sup 1}H NMR (nuclear magnetic resonance spectroscopy) has been used for metabolomic analysis of 'Riesling' and 'Mueller-Thurgau' white wines from the German Palatinate region. Diverse two-dimensional NMR techniques have been applied for the identification of metabolites, including phenolics. It is shown that sensory analysis correlates with NMR-based metabolic profiles of wine. {sup 1}H NMR data in combination with multivariate data analysis methods, like principal component analysis (PCA), partial least squares projections to latent structures (PLS), and bidirectional orthogonal projections to latent structures (O2PLS) analysis, were employed in an attempt to identify the metabolites responsible for the taste of wine, using a non-targeted approach. The high quality wines were characterized by elevated levels of compounds like proline, 2,3-butanediol, malate, quercetin, and catechin. Characterization of wine based on type and vintage was also done using orthogonal projections to latent structures (OPLS) analysis. 'Riesling' wines were characterized by higher levels of catechin, caftarate, valine, proline, malate, and citrate whereas compounds like quercetin, resveratrol, gallate, leucine, threonine, succinate, and lactate, were found discriminating for 'Mueller-Thurgau'. The wines from 2006 vintage were dominated by leucine, phenylalanine, citrate, malate, and phenolics, while valine, proline, alanine, and succinate were predominantly present in the 2007 vintage. Based on these results, it can be postulated the NMR-based metabolomics offers an easy and comprehensive analysis of wine and in combination with multivariate data analyses can be used to investigate the source of the wines and to predict certain sensory aspects of wine.
International Nuclear Information System (INIS)
Ali, Kashif; Maltese, Federica; Toepfer, Reinhard; Choi, Young Hae; Verpoorte, Robert
2011-01-01
1 H NMR (nuclear magnetic resonance spectroscopy) has been used for metabolomic analysis of ‘Riesling’ and ‘Mueller-Thurgau’ white wines from the German Palatinate region. Diverse two-dimensional NMR techniques have been applied for the identification of metabolites, including phenolics. It is shown that sensory analysis correlates with NMR-based metabolic profiles of wine. 1 H NMR data in combination with multivariate data analysis methods, like principal component analysis (PCA), partial least squares projections to latent structures (PLS), and bidirectional orthogonal projections to latent structures (O2PLS) analysis, were employed in an attempt to identify the metabolites responsible for the taste of wine, using a non-targeted approach. The high quality wines were characterized by elevated levels of compounds like proline, 2,3-butanediol, malate, quercetin, and catechin. Characterization of wine based on type and vintage was also done using orthogonal projections to latent structures (OPLS) analysis. ‘Riesling’ wines were characterized by higher levels of catechin, caftarate, valine, proline, malate, and citrate whereas compounds like quercetin, resveratrol, gallate, leucine, threonine, succinate, and lactate, were found discriminating for ‘Mueller-Thurgau’. The wines from 2006 vintage were dominated by leucine, phenylalanine, citrate, malate, and phenolics, while valine, proline, alanine, and succinate were predominantly present in the 2007 vintage. Based on these results, it can be postulated the NMR-based metabolomics offers an easy and comprehensive analysis of wine and in combination with multivariate data analyses can be used to investigate the source of the wines and to predict certain sensory aspects of wine.
International Nuclear Information System (INIS)
Lee, Lucille N.; Barnswell, Carlton; Torre, Taryn; Fearn, Paul; Kattan, Michael; Potters, Louis
2002-01-01
Purpose: To compare PSA relapse-free survival (PSA-RFS) between African-American (AA) and white American (WA) males treated with permanent prostate brachytherapy (PPB) for clinically localized prostate cancer. Methods and materials: One thousand eighty-one consecutive patients, including 246 African-Americans, underwent PPB with 103 Pd or 125 I, alone or with external beam radiation therapy between September 1992 and September 1999. Computer-generated matching was performed to create two identical cohorts of WA and AA males, based on the use of neoadjuvant androgen ablation (NAAD), pretreatment PSA, and Gleason score. Presenting characteristics were used to define risk groups, as follows: Low risk had PSA ≤10 and Gleason score ≤6, intermediate risk had PSA >10 or Gleason score ≥7, and high risk had PSA >10 and Gleason score ≥7. PSA-RFS was calculated using the Kattan modification of the ASTRO definition, and the log-rank test was used to compare Kaplan-Meier PSA-RFS curves. Univariate and multivariate analyses were performed to determine predictors of PSA-RFS. Results: Overall, univariate analysis revealed that AA males at presentation had lower disease stage (p=0.01), had lower Gleason scores (p=0.017), were younger (p=0.001), and were more likely to receive NAAD (p=0.001) than their WA counterparts. There were no differences in pretreatment PSA, isotope selection, use of external beam radiation therapy, median follow-up, or risk group classification between AA and WA males. Pretreatment PSA and Gleason score were significant predictors of PSA-RFS in multivariate analysis, and race was not significant. There was no significant difference between the 5-year PSA-RFS for AA males (84.0%) and the matched cohort of WA males (81.2%) (p=0.384). Race was not a predictor of 5-year PSA-RFS among patients treated with or without NAAD and within low-, intermediate-, and high-risk groups. Conclusion: Race is not an independent predictor of 5-year PSA-RFS in patients
Porta, Alberto; Bari, Vlasta; Marchi, Andrea; De Maria, Beatrice; Cysarz, Dirk; Van Leeuwen, Peter; Takahashi, Anielle C. M.; Catai, Aparecida M.; Gnecchi-Ruscone, Tomaso
2015-01-01
Two diverse complexity metrics quantifying time irreversibility and local prediction, in connection with a surrogate data approach, were utilized to detect nonlinear dynamics in short heart period (HP) variability series recorded in fetuses, as a function of the gestational period, and in healthy humans, as a function of the magnitude of the orthostatic challenge. The metrics indicated the presence of two distinct types of nonlinear HP dynamics characterized by diverse ranges of time scales. These findings stress the need to render more specific the analysis of nonlinear components of HP dynamics by accounting for different temporal scales. PMID:25806002
Directory of Open Access Journals (Sweden)
Linglong Cao
2015-04-01
Full Text Available Sediments in estuary areas are recognized as the ultimate reservoirs for numerous contaminants, e.g., toxic metals. Multivariate analyses by chemometric evaluation were performed to classify metal ions (Cu, Zn, As, Cr, Pb, Ni and Cd in superficial sediments from Lingdingyang Bay and to determine whether or not there were potential contamination risks based on the BCR sequential extraction scheme. The results revealed that Cd was mainly in acid-soluble form with an average of 75.99% of its total contents and thus of high potential availability, indicating significant anthropogenic sources, while Cr, As, Ni were enriched in the residual fraction which could be considered as the safest ingredients to the environment. According to the proportion of secondary to primary phases (KRSP, Cd had the highest bioavailable fraction and represented high or very high risk, followed by Pb and Cu with medium risks in most of samples. The combined evaluation of the Pollution Load Index (PLI and the mean Effect Range Median Quotient (mERM-Q highlighted that the greatest potential environmental risk area was in the northwest of Lingdingyang Bay. Almost all of the sediments had a 21% probability of toxicity. Additionally, Principal Component Analysis (PCA revealed that the survey region was significantly affected by two main sources of anthropogenic contributions: PC1 showed increased loadings of variables in acid-soluble and reducible fractions that were consistent with the input from industrial wastes (such as manufacturing, metallurgy, chemical industry and domestic sewages; PC2 was characterized by increased loadings of variables in residual fraction that could be attributed to leaching and weathering of parent rocks. The results obtained demonstrated the need for appropriate remediation measures to alleviate soil pollution problem due to the more aggregation of potentially risky metals. Therefore, it is of crucial significance to implement the targeted
Uncertainty and sensitivity analyses of ballast life-cycle cost and payback period
Mcmahon, James E.
2000-01-01
The paper introduces an innovative methology for evaluating the relative significance of energy-efficient technologies applied to fluorescent lamp ballasts. The method involves replacing the point estimates of life cycle cost of the ballasts with uncertainty distributions reflecting the whole spectrum of possible costs, and the assessed probability associated with each value. The results of uncertainty and sensitivity analyses will help analysts reduce effort in data collection and carry on a...
Karkar , Sami; Vergez , Christophe; Cochelin , Bruno
2012-01-01
International audience; We propose a new approach based on numerical continuation and bifurcation analysis for the study of physical models of instruments that produce self- sustained oscillation. Numerical continuation consists in following how a given solution of a set of equations is modified when one (or several) parameter of these equations are allowed to vary. Several physical models (clarinet, saxophone, and violin) are formulated as nonlinear dynamical systems, whose periodic solution...
Zhou, Hong; Melloni, Lucia; Poeppel, David; Ding, Nai
2016-01-01
Brain activity can follow the rhythms of dynamic sensory stimuli, such as speech and music, a phenomenon called neural entrainment. It has been hypothesized that low-frequency neural entrainment in the neural delta and theta bands provides a potential mechanism to represent and integrate temporal information. Low-frequency neural entrainment is often studied using periodically changing stimuli and is analyzed in the frequency domain using the Fourier analysis. The Fourier analysis decomposes a periodic signal into harmonically related sinusoids. However, it is not intuitive how these harmonically related components are related to the response waveform. Here, we explain the interpretation of response harmonics, with a special focus on very low-frequency neural entrainment near 1 Hz. It is illustrated why neural responses repeating at f Hz do not necessarily generate any neural response at f Hz in the Fourier spectrum. A strong neural response at f Hz indicates that the time scales of the neural response waveform within each cycle match the time scales of the stimulus rhythm. Therefore, neural entrainment at very low frequency implies not only that the neural response repeats at f Hz but also that each period of the neural response is a slow wave matching the time scale of a f Hz sinusoid.
Uncertainty and sensitivity analyses of ballast life-cycle cost and payback period
Energy Technology Data Exchange (ETDEWEB)
McMahon, James E.; Liu, Xiaomin; Turiel, Ike; Hakim, Sajid; Fisher, Diane
2000-06-01
The paper introduces an innovative methodology for evaluating the relative significance of energy-efficient technologies applied to fluorescent lamp ballasts. The method involves replacing the point estimates of life cycle cost of the ballasts with uncertainty distributions reflecting the whole spectrum of possible costs, and the assessed probability associated with each value. The results of uncertainty and sensitivity analyses will help analysts reduce effort in data collection and carry on analysis more efficiently. These methods also enable policy makers to gain an insightful understanding of which efficient technology alternatives benefit or cost what fraction of consumers, given the explicit assumptions of the analysis.
Simons, Monique; de Vet, Emely; Chinapaw, Mai Jm; de Boer, Michiel; Seidell, Jacob C; Brug, Johannes
2014-04-04
Playing video games contributes substantially to sedentary behavior in youth. A new generation of video games-active games-seems to be a promising alternative to sedentary games to promote physical activity and reduce sedentary behavior. At this time, little is known about correlates of active and non-active gaming among adolescents. The objective of this study was to examine potential personal, social, and game-related correlates of both active and non-active gaming in adolescents. A survey assessing game behavior and potential personal, social, and game-related correlates was conducted among adolescents (12-16 years, N=353) recruited via schools. Multivariable, multilevel logistic regression analyses, adjusted for demographics (age, sex and educational level of adolescents), were conducted to examine personal, social, and game-related correlates of active gaming ≥1 hour per week (h/wk) and non-active gaming >7 h/wk. Active gaming ≥1 h/wk was significantly associated with a more positive attitude toward active gaming (OR 5.3, CI 2.4-11.8; Pgames (OR 0.30, CI 0.1-0.6; P=.002), a higher score on habit strength regarding gaming (OR 1.9, CI 1.2-3.2; P=.008) and having brothers/sisters (OR 6.7, CI 2.6-17.1; Pgame engagement (OR 0.95, CI 0.91-0.997; P=.04). Non-active gaming >7 h/wk was significantly associated with a more positive attitude toward non-active gaming (OR 2.6, CI 1.1-6.3; P=.035), a stronger habit regarding gaming (OR 3.0, CI 1.7-5.3; P7 h/wk. Active gaming is most strongly (negatively) associated with attitude with respect to non-active games, followed by observed active game behavior of brothers and sisters and attitude with respect to active gaming (positive associations). On the other hand, non-active gaming is most strongly associated with observed non-active game behavior of friends, habit strength regarding gaming and attitude toward non-active gaming (positive associations). Habit strength was a correlate of both active and non-active gaming
de Vet, Emely; Chinapaw, Mai JM; de Boer, Michiel; Seidell, Jacob C; Brug, Johannes
2014-01-01
Background Playing video games contributes substantially to sedentary behavior in youth. A new generation of video games—active games—seems to be a promising alternative to sedentary games to promote physical activity and reduce sedentary behavior. At this time, little is known about correlates of active and non-active gaming among adolescents. Objective The objective of this study was to examine potential personal, social, and game-related correlates of both active and non-active gaming in adolescents. Methods A survey assessing game behavior and potential personal, social, and game-related correlates was conducted among adolescents (12-16 years, N=353) recruited via schools. Multivariable, multilevel logistic regression analyses, adjusted for demographics (age, sex and educational level of adolescents), were conducted to examine personal, social, and game-related correlates of active gaming ≥1 hour per week (h/wk) and non-active gaming >7 h/wk. Results Active gaming ≥1 h/wk was significantly associated with a more positive attitude toward active gaming (OR 5.3, CI 2.4-11.8; Pgames (OR 0.30, CI 0.1-0.6; P=.002), a higher score on habit strength regarding gaming (OR 1.9, CI 1.2-3.2; P=.008) and having brothers/sisters (OR 6.7, CI 2.6-17.1; Pgame engagement (OR 0.95, CI 0.91-0.997; P=.04). Non-active gaming >7 h/wk was significantly associated with a more positive attitude toward non-active gaming (OR 2.6, CI 1.1-6.3; P=.035), a stronger habit regarding gaming (OR 3.0, CI 1.7-5.3; P7 h/wk. Active gaming is most strongly (negatively) associated with attitude with respect to non-active games, followed by observed active game behavior of brothers and sisters and attitude with respect to active gaming (positive associations). On the other hand, non-active gaming is most strongly associated with observed non-active game behavior of friends, habit strength regarding gaming and attitude toward non-active gaming (positive associations). Habit strength was a
Gruszczynska, M.; Rosat, S.; Klos, A.; Bogusz, J.
2017-12-01
In this study, Singular Spectrum Analysis (SSA) along with its multivariate extension MSSA (Multichannel SSA) were used to estimate long-term trend and gravimetric factor at the Chandler wobble frequency from superconducting gravimeter (SG) records. We have used data from seven stations located worldwide and contributing to the International Geodynamics and Earth Tides Service (IGETS). The timespan ranged from 15 to 19 years. Before applying SSA and MSSA, we had removed local tides, atmospheric (ECMWF data), hydrological (MERRA2 products) loadings and non-tidal ocean loading (ECCO2 products) effects. In the first part of analysis, we used the SSA approach in order to estimate the long-term trends from SG observations. We use the technique based on the classical Karhunen-Loève spectral decomposition of time series into long-term trend, oscillations and noise. In the second part, we present the determination of common time-varying pole tide (annual and Chandler wobble) to estimate gravimetric factor from SG time series using the MSSA approach. The presented method takes advantage over traditional methods like Least Squares Estimation by determining common modes of variability which reflect common geophysical field. We adopted a 6-year lag-window as the optimal length to extract common seasonal signals and the Chandler components of the Earth polar motion. The signals characterized by annual and Chandler wobble account for approximately 62% of the total variance of residual SG data. Then, we estimated the amplitude factors and phase lags of Chandler wobble with respect to the IERS (International Earth Rotation and Reference Systems Service) polar motion observations. The resulting gravimetric factors at the Chandler Wobble period are finally compared with previously estimates. A robust estimate of the gravimetric Earth response to the Chandlerian component of the polar motion is required to better constrain the mantle anelasticity at this frequency and hence the
Bernard, Jean-Marc; Poitrenaud, Sébastien
2006-01-01
We propose a new method for simplifying the Galois lattice associated to a binary questionnaire (n units classified according to q binary questions). The method consists in weakening the implications borne by the lattice into quasi-implications. At the descriptive level, the method involves a new measure for quasi-implications (the "multivariate implicative index") which satisfies some requirements of invariance by logical equivalence. At the inductive level, uncertainty about the patterns' t...
Czech Academy of Sciences Publication Activity Database
Tollenaere, C.; Bryja, Josef; Galan, M.; Cadet, P.; Deter, J.; Chaval, Y.; Berthier, K.; Ribas Salvador, A.; Voutilainen, L.; Laakkonen, J.; Henttonen, H.; Cosson, J.-F.; Charbonnel, N.
2008-01-01
Roč. 21, č. 5 (2008), s. 1307-1320 ISSN 1010-061X EU Projects: European Commission(XE) 10284 - EDEN Institutional research plan: CEZ:AV0Z60930519 Keywords : co-inertia * DQA and DRB MHC gen es * immunogenetics * multivariate analysis * parasite-mediated balancing selection Subject RIV: EB - Gen etics ; Molecular Biology Impact factor: 3.471, year: 2008
Jåstad, Eirik O; Torheim, Turid; Villeneuve, Kathleen M; Kvaal, Knut; Hole, Eli O; Sagstuen, Einar; Malinen, Eirik; Futsaether, Cecilia M
2017-09-28
The amino acid l-α-alanine is the most commonly used material for solid-state electron paramagnetic resonance (EPR) dosimetry, due to the formation of highly stable radicals upon irradiation, with yields proportional to the radiation dose. Two major alanine radical components designated R1 and R2 have previously been uniquely characterized from EPR and electron-nuclear double resonance (ENDOR) studies as well as from quantum chemical calculations. There is also convincing experimental evidence of a third minor radical component R3, and a tentative radical structure has been suggested, even though no well-defined spectral signature has been observed experimentally. In the present study, temperature dependent EPR spectra of X-ray irradiated polycrystalline alanine were analyzed using five multivariate methods in further attempts to understand the composite nature of the alanine dosimeter EPR spectrum. Principal component analysis (PCA), maximum likelihood common factor analysis (MLCFA), independent component analysis (ICA), self-modeling mixture analysis (SMA), and multivariate curve resolution (MCR) were used to extract pure radical spectra and their fractional contributions from the experimental EPR spectra. All methods yielded spectral estimates resembling the established R1 spectrum. Furthermore, SMA and MCR consistently predicted both the established R2 spectrum and the shape of the R3 spectrum. The predicted shape of the R3 spectrum corresponded well with the proposed tentative spectrum derived from spectrum simulations. Thus, results from two independent multivariate data analysis techniques strongly support the previous evidence that three radicals are indeed present in irradiated alanine samples.
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.
Energy Technology Data Exchange (ETDEWEB)
Verloh, N.; Haimerl, M.; Stroszczynski, C.; Fellner, C.; Wiggermann, P. [University Hospital Regensburg (Germany). Dept. of Radiology; Zeman, F. [University Hospital Regensburg (Germany). Center for Clinical Trials; Teufel, A. [University Hospital Regensburg (Germany). Dept. of Gastroenterology; Lang, S. [University Hospital Regensburg (Germany). Dept. of Surgery
2015-01-15
The purpose of this study was to identify clinical factors influencing Gd-EOB-DTPA liver uptake in patients with healthy liver parenchyma. A total of 124 patients underwent contrast-enhanced MRI with a hepatocyte-specific contrast agent at 3T. T1-weighted volume interpolated breath-hold examination (VIBE) sequences with fat suppression were acquired before and 20 minutes after contrast injection. The relative enhancement (RE) between plain and contrast-enhanced signal intensity was calculated. Simple and multiple linear regression analyses were performed to evaluate clinical factors influencing the relative enhancement. Patients were subdivided into three groups according to their relative liver enhancement (HRE, RE ≥ 100 %; MRE, 100 % > RE > 50 %; NRE, RE ≤ 50 %) and were analyzed according to the relevant risk factors. Simple regression analyses revealed patient age, transaminases (AST, ALT, GGT), liver, spleen and delta-liver volume (the difference between the volumetrically measured liver volume and the estimated liver volume based on body weight) as significant factors influencing relative enhancement. In the multiple analysis the transaminase AST, spleen and delta liver volume remained significant factors influencing relative enhancement. Delta liver volume showed a significant difference between all analyzed groups. Liver enhancement in the hepatobiliary phase depends on a variety of factors. Body weight-adapted administration of Gd-EOB-DTPA may lead to inadequate liver enhancement after 20 minutes especially when the actual liver volume differs from the expected volume.
Energy Technology Data Exchange (ETDEWEB)
Otter, Sophie [Department of Clinical Oncology, Royal Marsden Hospital, Sutton and London (United Kingdom); Schick, Ulrike; Gulliford, Sarah [Department of Clinical Oncology, Royal Marsden Hospital, Sutton and London (United Kingdom); The Institute of Cancer Research, London (United Kingdom); Lal, Punita [Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow India (India); Franceschini, Davide [Department of Radiotherapy and Radiosurgery, Humanitas Research Hospital, Milan (Italy); Newbold, Katie; Nutting, Christopher; Harrington, Kevin [Department of Clinical Oncology, Royal Marsden Hospital, Sutton and London (United Kingdom); The Institute of Cancer Research, London (United Kingdom); Bhide, Shreerang, E-mail: shreerang.bhide@icr.ac.uk [Department of Clinical Oncology, Royal Marsden Hospital, Sutton and London (United Kingdom); The Institute of Cancer Research, London (United Kingdom); Department of Radiotherapy and Radiosurgery, Humanitas Research Hospital, Milan (Italy)
2015-11-01
Purpose: The study aimed to apply the atlas of complication incidence (ACI) method to patients receiving radical treatment for head and neck squamous cell carcinomas (HNSCC), to generate constraints based on dose-volume histograms (DVHs), and to identify clinical and dosimetric parameters that predict the risk of grade 3 oral mucositis (g3OM) and pharyngeal dysphagia (g3PD). Methods and Materials: Oral and pharyngeal mucosal DVHs were generated for 253 patients who received radiation (RT) or chemoradiation (CRT). They were used to produce ACI for g3OM and g3PD. Multivariate analysis (MVA) of the effect of dosimetry, clinical, and patient-related variables was performed using logistic regression and bootstrapping. Receiver operating curve (ROC) analysis was also performed, and the Youden index was used to find volume constraints that discriminated between volumes that predicted for toxicity. Results: We derived statistically significant dose-volume constraints for g3OM over the range v28 to v70. Only 3 statistically significant constraints were derived for g3PD v67, v68, and v69. On MVA, mean dose to the oral mucosa predicted for g3OM and concomitant chemotherapy and mean dose to the inferior constrictor (IC) predicted for g3PD. Conclusions: We have used the ACI method to evaluate incidences of g3OM and g3PD and ROC analysis to generate constraints to predict g3OM and g3PD derived from entire individual patient DVHs. On MVA, the strongest predictors were radiation dose (for g3OM) and concomitant chemotherapy (for g3PD).
Kjellenberg, Lars; Granstedt, Artur
2015-09-15
The aim of this paper was to present results from two long term field experiments comparing potato samples from conventional farming systems with samples from biodynamic farming systems. The principal component analyses (PCA), consistently exhibited differences between potato samples from the two farming systems. According to the PCA, potato samples treated with inorganic fertilizers exhibited a variation positively related to amounts of crude protein, yield, cooking or tissue discoloration and extract decomposition. Potato samples treated according to biodynamic principles, with composted cow manure, were more positively related to traits such as Quality- and EAA-indices, dry matter content, taste quality, relative proportion of pure protein and biocrystallization value. Distinctions between years, crop rotation and cultivars used were sometimes more significant than differences between manuring systems. Grown after barley the potato crop exhibited better quality traits compared to when grown after ley in both the conventional and the biodynamic farming system.
Directory of Open Access Journals (Sweden)
Lars Kjellenberg
2015-09-01
Full Text Available The aim of this paper was to present results from two long term field experiments comparing potato samples from conventional farming systems with samples from biodynamic farming systems. The principal component analyses (PCA, consistently exhibited differences between potato samples from the two farming systems. According to the PCA, potato samples treated with inorganic fertilizers exhibited a variation positively related to amounts of crude protein, yield, cooking or tissue discoloration and extract decomposition. Potato samples treated according to biodynamic principles, with composted cow manure, were more positively related to traits such as Quality- and EAA-indices, dry matter content, taste quality, relative proportion of pure protein and biocrystallization value. Distinctions between years, crop rotation and cultivars used were sometimes more significant than differences between manuring systems. Grown after barley the potato crop exhibited better quality traits compared to when grown after ley in both the conventional and the biodynamic farming system.
DEFF Research Database (Denmark)
Edberg, Anna; Freyhult, Eva; Sand, Salomon
- and inter-national data excerpts. For example, major PCA loadings helped deciphering both shared and disparate features, relating to food groups, across Danish and Swedish preschool consumers. Data interrogation, reliant on the above-mentioned composite techniques, disclosed one outlier dietary prototype...... prototype with the latter property was identified also in the Danish data material, but without low consumption of Vegetables or Fruit & berries. The second MDA-type of data interrogation involved Supervised Learning, also known as Predictive Modelling. These exercises involved the Random Forest (RF...... not elaborated on in-depth, output from several analyses suggests a preference for energy-based consumption data for Cluster Analysis and Predictive Modelling, over those appearing as weight....
International Nuclear Information System (INIS)
El-Arabi, A.M.Abd El-Gabar M.; Khalifa, Ibrahim H.
2002-01-01
Factor and cluster analyses as well as the Pearson correlation coefficient have been applied to geochemical data obtained from phosphorite and phosphatic rocks of Duwi Formation exposed at the Red Sea coast, Nile Valley and Western Desert. Sixty-six samples from a total of 71 collected samples were analysed for SiO 2 , TiO 2 , Al 2 O 3 , Fe 2 O 3 , CaO, MgO, Na 2 O, K 2 O, P 2 O 5 , Sr, U and Pb by XRF and their mineral constituents were determined by the use of XRD techniques. In addition, the natural radioactivity of the phosphatic samples due to their uranium, thorium and potassium contents was measured by gamma-spectrometry.The uranium content in the phosphate rocks with P 2 O 5 >15% (average of 106.6 ppm) is higher than in rocks with P 2 O 5 2 O 5 and CaO, whereas it is not related to changes in SiO 2 , TiO 2 , Al 2 O 3 , Fe 2 O 3 , MgO, Na 2 O and K 2 O concentrations.Factor analysis and the Pearson correlation coefficient revealed that uranium behaves geochemically in different ways in the phosphatic sediments and phosphorites in the Red Sea, Nile Valley and Western Desert. In the Red Sea and Western Desert phosphorites, uranium occurs mainly in oxidized U 6+ state where it seems to be fixed by the phosphate ion, forming secondary uranium phosphate minerals such as phosphuranylite.In the Nile Valley phosphorites, ionic substitution of Ca 2+ by U 4+ is the main controlling factor in the concentration of uranium in phosphate rocks. Moreover, fixation of U 6+ by phosphate ion and adsorption of uranium on phosphate minerals play subordinate roles
Armaş, Iuliana; Mendes, Diana A.; Popa, Răzvan-Gabriel; Gheorghe, Mihaela; Popovici, Diana
2017-03-01
The aim of this exploratory research is to capture spatial evolution patterns in the Bucharest metropolitan area using sets of single polarised synthetic aperture radar (SAR) satellite data and multi-temporal radar interferometry. Three sets of SAR data acquired during the years 1992-2010 from ERS-1/-2 and ENVISAT, and 2011-2014 from TerraSAR-X satellites were used in conjunction with the Small Baseline Subset (SBAS) and persistent scatterers (PS) high-resolution multi-temporal interferometry (InSAR) techniques to provide maps of line-of-sight displacements. The satellite-based remote sensing results were combined with results derived from classical methodologies (i.e., diachronic cartography) and field research to study possible trends in developments over former clay pits, landfill excavation sites, and industrial parks. The ground displacement trend patterns were analysed using several linear and nonlinear models, and techniques. Trends based on the estimated ground displacement are characterised by long-term memory, indicated by low noise Hurst exponents, which in the long-term form interesting attractors. We hypothesize these attractors to be tectonic stress fields generated by transpressional movements.
Greene, LaVana; Elzey, Brianda; Franklin, Mariah; Fakayode, Sayo O
2017-03-05
The negative health impact of polycyclic aromatic hydrocarbons (PAHs) and differences in pharmacological activity of enantiomers of chiral molecules in humans highlights the need for analysis of PAHs and their chiral analogue molecules in humans. Herein, the first use of cyclodextrin guest-host inclusion complexation, fluorescence spectrophotometry, and chemometric approach to PAH (anthracene) and chiral-PAH analogue derivatives (1-(9-anthryl)-2,2,2-triflouroethanol (TFE)) analyses are reported. The binding constants (K b ), stoichiometry (n), and thermodynamic properties (Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS)) of anthracene and enantiomers of TFE-methyl-β-cyclodextrin (Me-β-CD) guest-host complexes were also determined. Chemometric partial-least-square (PLS) regression analysis of emission spectra data of Me-β-CD-guest-host inclusion complexes was used for the determination of anthracene and TFE enantiomer concentrations in Me-β-CD-guest-host inclusion complex samples. The values of calculated K b and negative ΔG suggest the thermodynamic favorability of anthracene-Me-β-CD and enantiomeric of TFE-Me-β-CD inclusion complexation reactions. However, anthracene-Me-β-CD and enantiomer TFE-Me-β-CD inclusion complexations showed notable differences in the binding affinity behaviors and thermodynamic properties. The PLS regression analysis resulted in square-correlation-coefficients of 0.997530 or better and a low LOD of 3.81×10 -7 M for anthracene and 3.48×10 -8 M for TFE enantiomers at physiological conditions. Most importantly, PLS regression accurately determined the anthracene and TFE enantiomer concentrations with an average low error of 2.31% for anthracene, 4.44% for R-TFE and 3.60% for S-TFE. The results of the study are highly significant because of its high sensitivity and accuracy for analysis of PAH and chiral PAH analogue derivatives without the need of an expensive chiral column, enantiomeric resolution, or use of a polarized
Kołaczek, B.; Kosek, W.; Galas, R.
Series of BIH astrometric (BIH-ASTR) pole coordinates and of CSR LAGEOS laser ranging (CSR-LALAR) pole coordinates determined in the MERIT Campaign in the years 1972 - 1986, 1983 - 1986, respectively, have been filtered by different band pass filters consisting of the law pass Gauss filter and of the high pass Butterworth filter. Filtered residuals were analysed by the MESA-Maximum Entropy Spectra Analysis and by the Ormsby narrow band pass filters in order to find numerically modeled signals approximating these residuals in the best way.
Yu, Peiqiang; Damiran, Daalkhaijav; Azarfar, Arash; Niu, Zhiyuan
2011-01-01
The objective of this study was to use DRIFT spectroscopy with uni- and multivariate molecular spectral analyses as a novel approach to detect molecular features of spectra mainly associated with carbohydrate in the co-products (wheat DDGS, corn DDGS, blend DDGS) from bioethanol processing in comparison with original feedstock (wheat (Triticum), corn (Zea mays)). The carbohydrates related molecular spectral bands included: A_Cell (structural carbohydrates, peaks area region and baseline: ca. 1485–1188 cm−1), A_1240 (structural carbohydrates, peak area centered at ca. 1240 cm−1 with region and baseline: ca. 1292–1198 cm−1), A_CHO (total carbohydrates, peaks region and baseline: ca. 1187–950 cm−1), A_928 (non-structural carbohydrates, peak area centered at ca. 928 cm−1 with region and baseline: ca. 952–910 cm−1), A_860 (non-structural carbohydrates, peak area centered at ca. 860 cm−1 with region and baseline: ca. 880–827 cm−1), H_1415 (structural carbohydrate, peak height centered at ca. 1415 cm−1 with baseline: ca. 1485–1188 cm−1), H_1370 (structural carbohydrate, peak height at ca. 1370 cm−1 with a baseline: ca. 1485–1188 cm−1). The study shows that the grains had lower spectral intensity (KM Unit) of the cellulosic compounds of A_1240 (8.5 vs. 36.6, P carbohydrate of A_928 (17.3 vs. 2.0) and A_860 (20.7 vs. 7.6) than their co-products from bioethanol processing. There were no differences (P > 0.05) in the peak area intensities of A_Cell (structural CHO) at 1292–1198 cm−1 and A_CHO (total CHO) at 1187–950 cm−1 with average molecular infrared intensity KM unit of 226.8 and 508.1, respectively. There were no differences (P > 0.05) in the peak height intensities of H_1415 and H_1370 (structural CHOs) with average intensities 1.35 and 1.15, respectively. The multivariate molecular spectral analyses were able to discriminate and classify between the corn and corn DDGS molecular spectra, but not wheat and wheat DDGS. This
Mathevet, T.; Joel, G.; Gottardi, F.; Nemoz, B.
2017-12-01
The aim of this communication is to present analyses of climate variability and change on snow water equivalent (SWE) observations, reconstructions (1900-2016) and scenarii (2020-2100) of a hundred of snow courses dissiminated within the french Alps. This issue became particularly important since a decade, in regions where snow variability had a large impact on water resources availability, poor snow conditions in ski resorts and artificial snow production. As a water resources manager in french mountainuous regions, EDF (french hydropower company) has developed and managed a hydrometeorological network since 1950. A recent data rescue research allowed to digitize long term SWE manual measurments of a hundred of snow courses within the french Alps. EDF have been operating an automatic SWE sensors network, complementary to the snow course network. Based on numerous SWE observations time-series and snow accumulation and melt model (Garavaglia et al., 2017), continuous daily historical SWE time-series have been reconstructed within the 1950-2016 period. These reconstructions have been extented to 1900 using 20 CR reanalyses (ANATEM method, Kuentz et al., 2015) and up to 2100 using GIEC Climate Change scenarii. Considering various mountainous areas within the french Alps, this communication focuses on : (1) long term (1900-2016) analyses of variability and trend of total precipitation, air temperature, snow water equivalent, snow line altitude, snow season length , (2) long term variability of hydrological regime of snow dominated watersheds and (3) future trends (2020 -2100) using GIEC Climate Change scenarii. Comparing historical period (1950-1984) to recent period (1984-2016), quantitative results within a region in the north Alps (Maurienne) shows an increase of air temperature by 1.2 °C, an increase of snow line height by 200m, a reduction of SWE by 200 mm/year and a reduction of snow season length by 15 days. These analyses will be extended from north to south
Multivariate analysis with LISREL
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.
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...
International Nuclear Information System (INIS)
Vila Socias, L.; Prats Pico, N.; Buxeda Garrigos, J.
2016-01-01
Amphorae were the most popular transport vessels used to ship wine and other commodities in Antiquity. This paper reports the results of the analyses of 38 samples of several Roman amphorae types recovered at Calonge (Gerona, Spain). Chemical composition of individuals have been obtained by means of XRF. Equivalent firing temperatures have been estimated by XRD and further refiring experiments have been performed. The results suggest the existence of two different clay pastes among individuals analyzed, most of which were fired at the range 900-1000 degree centigrade. Moreover, it is argued that craftsmen did not follow highly standardized paste preparation process during the first activity period of the workshop. The results are discussed in terms of production technology and standardization. (Author)
International Nuclear Information System (INIS)
Ablekovskaya, O.N.; Amvros'ev, A.P.
1999-01-01
The character and direction of structural transformations of blood capillaries of micro circulatory channel of 20-days white rat foetus in normal conditions and after single external 0,5 Gy dose irradiation by 10 and 14 days of embryogenesis were examined. Electron-microscopical, stereo logical and statistical analyses were used. The peculiarities of reactions of hemo capillaries and their cell structure to gamma-rays action in embryogenesis were revealed. It was shown the increase of diameters of capillaries, extension of section area of cytoplasm of endotheliocytes, diminution the size of nuclei of these cells. Polyploid endotheliocytes were found in the experimental conditions. Prenatal acute irradiation in low doses leaded to reduction of the number of microvessels and mitochondria in cytoplasm of cells of blood capillaries in ovary of rat foetus. These results revealed that low dose ionizing radiation changed the morphological expression of important synthetic, transport and energy processes in capillary cells of ovary in fetal period of ontogenesis
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
Methods of Multivariate Analysis
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
Network structure of multivariate time series.
Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito
2015-10-21
Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.
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...
Multivariate Time Series Search
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...
Energy Technology Data Exchange (ETDEWEB)
Perdreau, N.
2000-01-17
The electric conductivity of tin dioxide depends on the temperature of the material and on the nature and environment of the surrounding gas. This work shows that the treatment by multivariable analysis methods of electric conductance signals of one sensor allows to determine concentrations of binary or ternary mixtures of ethanol (0-80 ppm), carbon monoxide (0-300 ppm) and methane (0-1000 ppm). A part of this study has consisted of the design and the implementation of an automatic testing bench allowing to acquire the electric conductance of four sensors in thermal cycle and under gaseous cycles. It has also revealed some disturbing effects (humidity,..) of the measurement. Two techniques of sensor fabrication have been used to obtain conductances (depending of temperature) distinct for each gas, reproducible for the different sensors and enough stable with time to allow the exploitation of the signals by multivariable analysis methods (tin dioxide under the form of thin layers obtained by reactive evaporation or under the form of sintered powder bars). In a last part, it has been shown that the quantitative determination of gas by the application of chemo-metry methods is possible although the relation between the electric conductances in one part and the temperatures and concentrations in another part is non linear. Moreover, the modelling with the 'Partial Least Square' method and a pretreatment allows to obtain performance data comparable to those obtained with neural networks. (O.M.)
Essentials of multivariate data analysis
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
Multivariate process monitoring of EAFs
Energy Technology Data Exchange (ETDEWEB)
Sandberg, E.; Lennox, B.; Marjanovic, O.; Smith, K.
2005-06-01
Improved knowledge of the effect of scrap grades on the electric steelmaking process and optimised scrap loading practices increase the potential for process automation. As part of an ongoing programme, process data from four Scandinavian EAFs have been analysed, using the multivariate process monitoring approach, to develop predictive models for end point conditions such as chemical composition, yield and energy consumption. The models developed generally predict final Cr, Ni and Mo and tramp element contents well, but electrical energy consumption, yield and content of oxidisable and impurity elements (C, Si, Mn, P, S) are at present more difficult to predict. Potential scrap management applications of the prediction models are also presented. (author)
Multivariate Birkhoff interpolation
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...
Applied multivariate statistical analysis
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 ...
Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany
2016-07-01
Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.
Multivariate statistical methods a first course
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
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.
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...
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...
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.
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 ...
Multivariate calculus and geometry
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.
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)
Multivariate rational data fitting
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.
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.
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
Introduction to multivariate discrimination
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
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
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...
Directory of Open Access Journals (Sweden)
Edson Mauro Santos
2008-04-01
Full Text Available The experiment was conducted with objective of evaluating the tanzania grass, using mult-trait procedures: discriminant function analysis and principal components analysis. The variables were: dry matter of leaf (DML, dry matter of the plant (DMP, nitrogen (N, phosphorous (P, magnesium (Mg, calcium (Ca and potassium (K in the aerial part and in the root of the grass, during the rain period. The plants were fertilized with four doses of N as bovine manure (0, 60, 120, 180 kg/ha. Main components analysis was capable to reduce the 15 original variables to 6 principal components, which the most important variables were calcium at root, potassium at leaf, potassium at plant, magnesium at leaf, phosphorous at leaf and phosphorous at root. The discriminant analysis had difficulties in classifying the fertilizer dose, especially related to the levels 60, 120 e 180 kg/ha.Foi realizado um experimento com objetivo de avaliar o capim-tanzânia, por meio da técnica de análise multivariada: análise discriminante e análise de componentes principais. As variáveis avaliadas foram: matéria seca foliar (MSF, matéria seca da planta (MSP, nitrogênio (N, fósforo (P, magnésio (Mg e cálcio (Ca na parte aérea e na raiz, da gramínea, realizados no período das chuvas (primavera/verão. As plantas foram adubadas com quatro doses de N na forma de chorume bovino (0, 60, 120, 180 kg/ha. A análise de componentes principais foi capaz de reduzir o número de variáveis originais de 15 para seis componentes principais, sendo que as variáveis mais importantes foram cálcio na raiz (CaR, potássio na folha (KF, potássio na planta (KP, magnésio na folha (MgF, fósforo na folha (PF e fósforo na raiz (PR. A análise discriminante encontrou dificuldades em classificar quanto à dose de adubo, principalmente em relação às doses 60, 120 e 180 kg/ha.
Ke, I-Chung
2012-01-01
Using both quantitative and qualitative content analysis of Taiwan's high-school English textbooks, this study aimed to investigate the projected roles of English in Taiwan's high-school English textbooks over the past 50 years. A total of 1072 lessons from 14 textbook versions dating from 1952 to 2009 were analysed. The results show that the…
Multivariable calculus with applications
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...
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...
International Nuclear Information System (INIS)
Lopez, Patricio; Cartajena, Isabel; Nunez, Lautaro
2013-01-01
This paper presents the results of isotope analysis (δ 13 C y δ 15 N) conducted on bone collagen found in Lama guanicoe and Lama glama remains from Tulan-85 and Tulan-54 archaeological sites. Both sites have been dated to the Early Formative Period (ca. 3,100-2,400 ap) and are located southeast of the Atacama Puna basin. Faunal samples were selected using anatomical and morphometric criteria. The results indicate divergences in the diets of both species, reflecting vegetation variation in the Tulan Quebrada caused by altitude differences and linked to hunting and herding areas [es
Multivariate statistical methods a primer
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
International Nuclear Information System (INIS)
Querré, G.; Calligaro, T.; Domínguez-Bella, S.; Cassen, S.
2014-01-01
PIXE analysis of archeological variscite beads and pendants from the Neolithic period that were excavated in Spain, Portugal and France and of variscite geological references samples from European occurrences were carried out from 1999 to 2013 in order to trace back the circulation of this precious gemstone over three millennia. Transformations of the AGLAE external beam system and progress in spectrum processing have induced some apparent compositional variation, affecting in particular the phosphorus/aluminum ratio. This long term evolution has been taken into account with the help of geostandards to build a large and coherent geochemical database of minor and trace elements in variscite. This database allowed us to determine the provenance of the raw material and thus of the circulation of the jewels
Energy Technology Data Exchange (ETDEWEB)
Querré, G., E-mail: guirec.querre@univ-rennes1.fr [Laboratoire Archéosciences, UMR 6566 CReAAH, Université de Rennes 1, Rennes (France); Calligaro, T. [Centre de Recherche et de Restauration des Musées de France, Palais du Louvre, Paris (France); Domínguez-Bella, S. [UGEA-PHAM, Departamento de Ciencias de la Tierra, Universidad de Cádiz (Spain); Cassen, S. [Laboratoire de Recherches Archéologiques, UMR 6566 CReAAH, Nantes (France)
2014-01-01
PIXE analysis of archeological variscite beads and pendants from the Neolithic period that were excavated in Spain, Portugal and France and of variscite geological references samples from European occurrences were carried out from 1999 to 2013 in order to trace back the circulation of this precious gemstone over three millennia. Transformations of the AGLAE external beam system and progress in spectrum processing have induced some apparent compositional variation, affecting in particular the phosphorus/aluminum ratio. This long term evolution has been taken into account with the help of geostandards to build a large and coherent geochemical database of minor and trace elements in variscite. This database allowed us to determine the provenance of the raw material and thus of the circulation of the jewels.
Shahzad, Khuram; Bionaz, Massimo; Trevisi, Erminio; Bertoni, Giuseppe; Rodriguez-Zas, Sandra L.; Loor, Juan J.
2014-01-01
Using published dairy cattle liver transcriptomics dataset along with novel blood biomarkers of liver function, metabolism, and inflammation we have attempted an integrative systems biology approach applying the classical functional enrichment analysis using DAVID, a newly-developed Dynamic Impact Approach (DIA), and an upstream gene network analysis using Ingenuity Pathway Analysis (IPA). Transcriptome data was generated from experiments evaluating the impact of prepartal plane of energy intake [overfed (OF) or restricted (RE)] on liver of dairy cows during the peripartal period. Blood biomarkers uncovered that RE vs. OF led to greater prepartal liver distress accompanied by a low-grade inflammation and larger proteolysis (i.e., higher haptoglobin, bilirubin, and creatinine). Post-partum the greater bilirubinaemia and lipid accumulation in OF vs. RE indicated a large degree of liver distress. The re-analysis of microarray data revealed that expression of >4,000 genes was affected by diet × time. The bioinformatics analysis indicated that RE vs. OF cows had a liver with a greater lipid and amino acid catabolic capacity both pre- and post-partum while OF vs. RE cows had a greater activation of pathways/functions related to triglyceride synthesis. Furthermore, RE vs. OF cows had a larger (or higher capacity to cope with) ER stress likely associated with greater protein synthesis/processing, and a higher activation of inflammatory-related functions. Liver in OF vs. RE cows had a larger cell proliferation and cell-to-cell communication likely as a response to the greater lipid accumulation. Analysis of upstream regulators indicated a pivotal role of several lipid-related transcription factors (e.g., PPARs, SREBPs, and NFE2L2) in priming the liver of RE cows to better face the early postpartal metabolic and inflammatory challenges. An all-encompassing dynamic model was proposed based on the findings. PMID:24914544
Multivariate statistics exercises and solutions
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.
Odoardi, Sara; Romolo, Francesco Saverio; Strano-Rossi, Sabina
2016-08-01
). In some mixtures of drugs - such as granules - 4-MEC and pentedrone were detected, also with traces of diphenidine on one occasion. In other cases 5-MeO-DALT, ethylphenidate and caffeine were mixed together. In one batch, the mixture was flephedrone and methoxethamine, whereas in another one the sample contained methylone, ethylone, methedrone, 4-fluoroamphetamine, 5-MeO-DALT and 5MeO-MiPT (N-methyl-N-isopropyl-5-methoxytryptamine). In 9 seizures, tablets shipped together with NPS were also found to contain sildenafil. The analyses performed on these seizures showed the presence of a wide number of NPS within the Italian boundaries coming from abroad, therefore this study confirms the threat for the public health, especially when the content of NPS being sold is not reported on the label or misleading. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Prospective surveillance of multivariate spatial disease data
Corberán-Vallet, A
2012-01-01
Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented. PMID:22534429
Directory of Open Access Journals (Sweden)
Francisco A. A. Miranda
1998-06-01
Full Text Available A polimerização de adesivo à base de cianoacrilato foi acompanhada por FT-IR durante 30 minutos. A aplicação das técnicas de estatística multivariada (análise de agrupamento hierárquico e a análise dos componentes principais aos espectros de infravermelho, permitiram uma melhor identificação das diferenças espectrais entre monômero e polímero e possibilitou, também, inferir que a quantidade de monômero e do mero no polímero se eqüivalem com seis minutos de polimerização. A técnica de infravermelho mostrou-se uma ferramenta adequada para o acompanhamento da cinética de reação de adesivo à base de cianoacrilato, que torna-se ainda mais eficiente quando associada às técnicas de estatística multivariada.The polymerization of a cyanoacrylate adhesive was accompanied by FT-IR during 30 minutes. The application of multivariate statistics techniques (Hierarchical Clusters Analyses and Principal Components on infrared spectra allowed a better identification of spectral differences between monomer and polymer and also permitted to infer that the quantity of monomer and of mer of the polymer are equal in six minutes polymerization (half-life. The infrared technique appeared as an apropriate tool for observing the kinetics of cyanoacrylate adhesive reaction, which becomes even more efficient when associated to multivariate statistics techniques.
Multivariate statistical analysis of wildfires in Portugal
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).
Modeling Covariance Breakdowns in Multivariate GARCH
Jin, Xin; Maheu, John M
2014-01-01
This paper proposes a flexible way of modeling dynamic heterogeneous covariance breakdowns in multivariate GARCH (MGARCH) models. During periods of normal market activity, volatility dynamics are governed by an MGARCH specification. A covariance breakdown is any significant temporary deviation of the conditional covariance matrix from its implied MGARCH dynamics. This is captured through a flexible stochastic component that allows for changes in the conditional variances, covariances and impl...
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
Model Checking Multivariate State Rewards
DEFF Research Database (Denmark)
Nielsen, Bo Friis; Nielson, Flemming; Nielson, Hanne Riis
2010-01-01
We consider continuous stochastic logics with state rewards that are interpreted over continuous time Markov chains. We show how results from multivariate phase type distributions can be used to obtain higher-order moments for multivariate state rewards (including covariance). We also generalise...
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
Multivariate covariance generalized linear models
DEFF Research Database (Denmark)
Bonat, W. H.; Jørgensen, Bent
2016-01-01
are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions......We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...
A primer of multivariate statistics
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
A multivariate analysis of Antarctic sea ice since 1979
Energy Technology Data Exchange (ETDEWEB)
Magalhaes Neto, Newton de; Evangelista, Heitor [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Tanizaki-Fonseca, Kenny [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Universidade Federal Fluminense (UFF), Dept. Analise Geoambiental, Inst. de Geociencias, Niteroi, RJ (Brazil); Penello Meirelles, Margareth Simoes [Universidade do Estado do Rio de Janeiro (UERJ)/Geomatica, Maracana, Rio de Janeiro, RJ (Brazil); Garcia, Carlos Eiras [Universidade Federal do Rio Grande (FURG), Laboratorio de Oceanografia Fisica, Rio Grande, RS (Brazil)
2012-03-15
Recent satellite observations have shown an increase in the total extent of Antarctic sea ice, during periods when the atmosphere and oceans tend to be warmer surrounding a significant part of the continent. Despite an increase in total sea ice, regional analyses depict negative trends in the Bellingshausen-Amundsen Sea and positive trends in the Ross Sea. Although several climate parameters are believed to drive the formation of Antarctic sea ice and the local atmosphere, a descriptive mechanism that could trigger such differences in trends are still unknown. In this study we employed a multivariate analysis in order to identify the response of the Antarctic sea ice with respect to commonly utilized climate forcings/parameters, as follows: (1) The global air surface temperature, (2) The global sea surface temperature, (3) The atmospheric CO{sub 2} concentration, (4) The South Annular Mode, (5) The Nino 3, (6) The Nino (3 + 4, 7) The Nino 4, (8) The Southern Oscillation Index, (9) The Multivariate ENSO Index, (10) the Total Solar Irradiance, (11) The maximum O{sub 3} depletion area, and (12) The minimum O{sub 3} concentration over Antarctica. Our results indicate that western Antarctic sea ice is simultaneously impacted by several parameters; and that the minimum, mean, and maximum sea ice extent may respond to a separate set of climatic/geochemical parameters. (orig.)
Multivariate and Spatial Visualisation of Archaeological Assemblages
Directory of Open Access Journals (Sweden)
Martin Sterry
2018-05-01
Full Text Available Multivariate analyses, in particular correspondence analysis (CA, have become a standard exploratory tool for analysing and interpreting variance in archaeological assemblages. While they have greatly helped analysts, they unfortunately remain abstract to the viewer, all the more so if the viewer has little or no experience with multivariate statistics. A second issue with these analyses can arise from the detachment of archaeological material from its geo-referenced location and typically considered only in terms of arbitrary classifications (e.g. North Europe, Central Europe, South Europe instead of the full range of local conditions (e.g. proximity to other assemblages, relationships with other spatial phenomena. This article addresses these issues by presenting a novel method for spatially visualising CA so that these analyses can be interpreted intuitively. The method works by transforming the resultant bi-plots of the CA into colour maps using the HSV colour model, in which the similarity and difference between assemblages directly corresponds to the similarity and difference of the colours used to display them. Utilising two datasets – ceramics from the excavations of the Roman fortress of Vetera I, and terra sigillata forms collected as part of 'The Samian Project' – the article demonstrates how the method is applied and how it can be used to draw out spatial and temporal trends.
Multivariate Approaches to Classification in Extragalactic Astronomy
Directory of Open Access Journals (Sweden)
Didier eFraix-Burnet
2015-08-01
Full Text Available Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono- or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.
Multivariate Drought Characterization in India for Monitoring and Prediction
Sreekumaran Unnithan, P.; Mondal, A.
2016-12-01
Droughts are one of the most important natural hazards that affect the society significantly in terms of mortality and productivity. The metric that is most widely used by the India Meteorological Department (IMD) to monitor and predict the occurrence, spread, intensification and termination of drought is based on the univariate Standardized Precipitation Index (SPI). However, droughts may be caused by the influence and interaction of many variables (such as precipitation, soil moisture, runoff, etc.), emphasizing the need for a multivariate approach for drought characterization. This study advocates and illustrates use of the recently proposed multivariate standardized drought index (MSDI) in monitoring and prediction of drought and assessing its concerned risk in the Indian region. MSDI combines information from multiple sources: precipitation and soil moisture, and has been deemed to be a more reliable drought index. All-India monthly rainfall and soil moisture data sets are analysed for the period 1980 to 2014 to characterize historical droughts using both the univariate indices, the precipitation-based SPI and the standardized soil moisture index (SSI), as well as the multivariate MSDI using parametric and non-parametric approaches. We confirm that MSDI can capture droughts of 1986 and 1990 that aren't detected by using SPI alone. Moreover, in 1987, MSDI indicated a higher severity of drought when a deficiency in both soil moisture and precipitation was encountered. Further, this study also explores the use of MSDI for drought forecasts and assesses its performance vis-à-vis existing predictions from the IMD. Future research efforts will be directed towards formulating a more robust standardized drought indicator that can take into account socio-economic aspects that also play a key role for water-stressed regions such as India.
Multivariate and semiparametric kernel regression
Härdle, Wolfgang; Müller, Marlene
1997-01-01
The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...
Multivariate Statistical Process Control Charts: An Overview
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...
Multivariate Generalized Multiscale Entropy Analysis
Directory of Open Access Journals (Sweden)
Anne Humeau-Heurtier
2016-11-01
Full Text Available Multiscale entropy (MSE was introduced in the 2000s to quantify systems’ complexity. MSE relies on (i a coarse-graining procedure to derive a set of time series representing the system dynamics on different time scales; (ii the computation of the sample entropy for each coarse-grained time series. A refined composite MSE (rcMSE—based on the same steps as MSE—also exists. Compared to MSE, rcMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy for short time series. The multivariate versions of MSE (MMSE and rcMSE (MrcMSE have also been introduced. In the coarse-graining step used in MSE, rcMSE, MMSE, and MrcMSE, the mean value is used to derive representations of the original data at different resolutions. A generalization of MSE was recently published, using the computation of different moments in the coarse-graining procedure. However, so far, this generalization only exists for univariate signals. We therefore herein propose an extension of this generalized MSE to multivariate data. The multivariate generalized algorithms of MMSE and MrcMSE presented herein (MGMSE and MGrcMSE, respectively are first analyzed through the processing of synthetic signals. We reveal that MGrcMSE shows better performance than MGMSE for short multivariate data. We then study the performance of MGrcMSE on two sets of short multivariate electroencephalograms (EEG available in the public domain. We report that MGrcMSE may show better performance than MrcMSE in distinguishing different types of multivariate EEG data. MGrcMSE could therefore supplement MMSE or MrcMSE in the processing of multivariate datasets.
Morphological assessment of Niger Kuri cattle using multivariate ...
African Journals Online (AJOL)
This work confirms that at type trait level Kuri cattle is a unique population within the West African taurine cattle group. The implementation of genetic analyses aiming at ascertaining the degree of uniqueness of the breed is advised. Keywords: Body measurements, Bos taurus, multivariate analyses, qualitative traits, West ...
A comparison of multivariate genome-wide association methods
DEFF Research Database (Denmark)
Galesloot, Tessel E; Van Steen, Kristel; Kiemeney, Lambertus A L M
2014-01-01
Joint association analysis of multiple traits in a genome-wide association study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six...... methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three...... for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between...
Applied multivariate statistics with R
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...
Multivariate stochastic simulation with subjective multivariate normal distributions
P. J. Ince; J. Buongiorno
1991-01-01
In many applications of Monte Carlo simulation in forestry or forest products, it may be known that some variables are correlated. However, for simplicity, in most simulations it has been assumed that random variables are independently distributed. This report describes an alternative Monte Carlo simulation technique for subjectively assesed multivariate normal...
Multivariate Matrix-Exponential Distributions
DEFF Research Database (Denmark)
Bladt, Mogens; Nielsen, Bo Friis
2010-01-01
be written as linear combinations of the elements in the exponential of a matrix. For this reason we shall refer to multivariate distributions with rational Laplace transform as multivariate matrix-exponential distributions (MVME). The marginal distributions of an MVME are univariate matrix......-exponential distributions. We prove a characterization that states that a distribution is an MVME distribution if and only if all non-negative, non-null linear combinations of the coordinates have a univariate matrix-exponential distribution. This theorem is analog to a well-known characterization theorem...
Fourier expansions and multivariable Bessel functions concerning radiation programmes
International Nuclear Information System (INIS)
Dattoli, G.; Richetta, M.; Torre, A.; Chiccoli, C.; Lorenzutta, S.; Maino, G.
1996-01-01
The link between generalized Bessel functions and other special functions is investigated using the Fourier series and the generalized Jacobi-Anger expansion. A new class of multivariable Hermite polynomials is then introduced and their relevance to physical problems discussed. As an example of the power of the method, applied to radiation physics, we analyse the role played by multi-variable Bessel functions in the description of radiation emitted by a charge constrained to a nonlinear oscillation. (author)
The Multivariate Gaussian Probability Distribution
DEFF Research Database (Denmark)
Ahrendt, Peter
2005-01-01
This technical report intends to gather information about the multivariate gaussian distribution, that was previously not (at least to my knowledge) to be found in one place and written as a reference manual. Additionally, some useful tips and tricks are collected that may be useful in practical ...
A "Model" Multivariable Calculus Course.
Beckmann, Charlene E.; Schlicker, Steven J.
1999-01-01
Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…
A correction term for the covariance of renewal-reward processes with multivariate rewards
Patch, B.; Nazarathy, Y.; Taimre, T.
We consider a renewal-reward process with multivariate rewards. Such a process is constructed from an i.i.d. sequence of time periods, to each of which there is associated a multivariate reward vector. The rewards in each time period may depend on each other and on the period length, but not on the
International Nuclear Information System (INIS)
Hirotsu, Yuko; Tsuge, Tadashi; Sano, Toshiaki; Takano, Kenichi; Gouda, Hidenori
2001-01-01
CRIEPI has been conducting detailed analyses of all human error incidents at domestic nuclear power plants (NPPs) collected from Japanese Licensee Event Reports (LERs) using J-HPES (Japanese version of HPES) as an analysis method. Results obtained by the analyses have been stored in J-HPES database. Since 1999, human error incidents have been selected from U.S. LERs, and they are analyzed using J-HPES. In this report, the results, which classified error action, cause, and preventive measure, are summarized for U.S. human error cases occurring in the period from 1992 to 1996. It was suggested as a result of classification that the categories of error action were almost the same as those of Japanese human error cases. Therefore, problems in the process of error action and checkpoints for preventing errors will be extracted by analyzing both U.S. and domestic human error cases. It was also suggested that the interrelations between error actions, causes, and organizational factors could be identified. While taking these suggestions into consideration, we will continue to analyze U.S. human error cases. (author)
Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto
2017-02-01
Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Naumann, Elise; Price, T Douglas; Richards, Michael P
2014-11-01
Human remains representing 33 individuals buried along the coast in northern Norway were analyzed for diet composition using collagen stable carbon and nitrogen isotope analysis. Where possible, both teeth and bone were included to investigate whether there were dietary changes from childhood to adulthood. A general shift was documented from the Merovingian Age 550-800 AD to the Viking Age AD 800-1050 (VA), with a heavier reliance on marine diet in the VA. Dietary life history data show that 15 individuals changed their diets through life with 11 of these having consumed more marine foods in the later years of life. In combination with (87) Sr/(86) Sr data, it is argued that at least six individuals possibly originated from inland areas and then moved to the coastal region where they were eventually interred. The trend is considered in relation to the increasing expansion of the marine fishing industry at this time, and it is suggested that results from isotope analyses reflect the expanding production and export of stockfish in this region. © 2014 Wiley Periodicals, Inc.
Likelihood estimators for multivariate extremes
Huser, Raphaë l; Davison, Anthony C.; Genton, Marc G.
2015-01-01
The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.
Sparse Linear Identifiable Multivariate Modeling
DEFF Research Database (Denmark)
Henao, Ricardo; Winther, Ole
2011-01-01
and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable......In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...
Improved multivariate polynomial factoring algorithm
International Nuclear Information System (INIS)
Wang, P.S.
1978-01-01
A new algorithm for factoring multivariate polynomials over the integers based on an algorithm by Wang and Rothschild is described. The new algorithm has improved strategies for dealing with the known problems of the original algorithm, namely, the leading coefficient problem, the bad-zero problem and the occurrence of extraneous factors. It has an algorithm for correctly predetermining leading coefficients of the factors. A new and efficient p-adic algorithm named EEZ is described. Bascially it is a linearly convergent variable-by-variable parallel construction. The improved algorithm is generally faster and requires less store then the original algorithm. Machine examples with comparative timing are included
Likelihood estimators for multivariate extremes
Huser, Raphaël
2015-11-17
The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.
Simulation of multivariate diffusion bridges
DEFF Research Database (Denmark)
Bladt, Mogens; Finch, Samuel; Sørensen, Michael
We propose simple methods for multivariate diffusion bridge simulation, which plays a fundamental role in simulation-based likelihood and Bayesian inference for stochastic differential equations. By a novel application of classical coupling methods, the new approach generalizes a previously...... proposed simulation method for one-dimensional bridges to the mulit-variate setting. First a method of simulating approzimate, but often very accurate, diffusion bridges is proposed. These approximate bridges are used as proposal for easily implementable MCMC algorithms that produce exact diffusion bridges...
Aspects of multivariate statistical theory
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
Application of multivariate techniques to analytical data on Aegean ceramics
International Nuclear Information System (INIS)
Bieber, A.M.; Brooks, D.W.; Harbottle, G.; Sayre, E.V.
1976-01-01
The general problems of data collection and handling for multivariate elemental analyses of ancient pottery are considered including such specific questions as the level of analytical precision required, the number and type of elements to be determined and the need for comprehensive multivariate statistical analysis of the collected data in contrast to element by element statistical analysis. The multivariate statistical procedures of clustering in a multidimensional space and determination of the numerical probabilities of specimens belonging to a group through calculation of the Mahalanobis distances for these specimens in multicomponent space are described together with supporting univariate statistical procedures used at Brookhaven. The application of these techniques to the data on Late Bronze Age Aegean pottery (largely previously analysed at Oxford and Brookhaven with some new specimens considered) have resulted in meaningful subdivisions of previously established groups. (author)
Peramalan Multivariate untuk Menentukan Harga Emas Global
Directory of Open Access Journals (Sweden)
David Christian
2016-12-01
Full Text Available Gold is one of the most enticing commodities and a very popular way of investing. Gold’s price is allegedly influenced by another factors such as US Dollar, oil’s price, inflation rate, and stock exchange so that its model is not only affected by its value. The aim of this research is to determine the best forecasting model and influencing factors to gold’s price. This research reviews the univariate modeling as a benchmark and comparison to the multivariate one. Univariate time series is modeled using the ARIMA model which indicates that the fluctuation of the gold prices are following the white noise. Gold’s multivariate modeling is built using the Vector Error Correction Model with oil’s price, US Dollar and Dow Jones indices, and inflation rate as its predictors. Research’s result shows that the VECM model has been able to model the gold’s price well and all factors investigated are influencing gold’s price. US Dollar and oil’s price are negatively correlated with gold’s price as the inflation rate is positively correlated. Dow Jones Index is positively correlated with gold’s price only at its first two periods
Investigation of intervertebral disc degeneration using multivariate FTIR spectroscopic imaging
Mader, Kerstin T.; Peeters, Mirte; Detiger, Suzanne E. L.; Helder, Marco N.; Smit, Theo H.; Le Maitre, Christine L.; Sammon, Chris
2016-01-01
Traditionally tissue samples are analysed using protein or enzyme specific stains on serial sections to build up a picture of the distribution of components contained within them. In this study we investigated the potential of multivariate curve resolution-alternating least squares (MCR-ALS) to
Lake Chini Water Quality Assessment Using Multivariate Approach
International Nuclear Information System (INIS)
Ahmad, A.K.; Shuhaimi, Othman M.; Lim, E.C.; Aziz, Z.A.
2013-01-01
An analysis was undertaken using the multivariate approach to determine the important water quality for shallow lake water quality assessment. Fourteen water quality parameters which includes biological, physical and chemical components were collected monthly over twelve month period. The data were analysed using factor analysis which involves identification of factor correlation, factor extraction and factor permutations. The first process involved the clustering of high correlation parameters into its respective factor and the removal of parameters that have more than one factor. Agglomerative hierarchy (HACA) and discriminant analysis (DA) were also used to exhibit the important factors that has significant influence on lake water quality. The analysis showed that Lake Chini water quality was determined by more than one factor. The results indicated that the biological and chemical (nutrients) components have significant influence in determining the lake water quality. The biological parameters namely BOD5, COD, chlorophyll a and chemical (nitrate and orthophosphate) are important parameters in Lake Chini. All analysis demonstrated the importance of biological and chemical water quality components in the determination of Lake Chini water quality. (author)
Multivariate calibration applied to the quantitative analysis of infrared spectra
Energy Technology Data Exchange (ETDEWEB)
Haaland, D.M.
1991-01-01
Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.
Multivariate methods for particle identification
Visan, Cosmin
2013-01-01
The purpose of this project was to evaluate several MultiVariate methods in order to determine which one, if any, offers better results in Particle Identification (PID) than a simple n$\\sigma$ cut on the response of the ALICE PID detectors. The particles considered in the analysis were Pions, Kaons and Protons and the detectors used were TPC and TOF. When used with the same input n$\\sigma$ variables, the results show similar perfoance between the Rectangular Cuts Optimization method and the simple n$\\sigma$ cuts. The method MLP and BDT show poor results for certain ranges of momentum. The KNN method is the best performing, showing similar results for Pions and Protons as the Cuts method, and better results for Kaons. The extension of the methods to include additional input variables leads to poor results, related to instabilities still to be investigated.
Acoustic multivariate condition monitoring - AMCM
Energy Technology Data Exchange (ETDEWEB)
Rosenhave, P E [Vestfold College, Maritime Dept., Toensberg (Norway)
1998-12-31
In Norway, Vestfold College, Maritime Department presents new opportunities for non-invasive, on- or off-line acoustic monitoring of rotating machinery such as off-shore pumps and diesel engines. New developments within acoustic sensor technology coupled with chemometric data analysis of complex signals now allow condition monitoring of hitherto unavailable flexibility and diagnostic specificity. Chemometrics paired with existing knowledge yields a new and powerful tool for condition monitoring. By the use of multivariate techniques and acoustics it is possible to quantify wear and tear as well as predict the performance of working components in complex machinery. This presentation describes the AMCM method and one result of a feasibility study conducted onboard the LPG/C `Norgas Mariner` owned by Norwegian Gas Carriers as (NGC), Oslo. (orig.) 6 refs.
Acoustic multivariate condition monitoring - AMCM
Energy Technology Data Exchange (ETDEWEB)
Rosenhave, P.E. [Vestfold College, Maritime Dept., Toensberg (Norway)
1997-12-31
In Norway, Vestfold College, Maritime Department presents new opportunities for non-invasive, on- or off-line acoustic monitoring of rotating machinery such as off-shore pumps and diesel engines. New developments within acoustic sensor technology coupled with chemometric data analysis of complex signals now allow condition monitoring of hitherto unavailable flexibility and diagnostic specificity. Chemometrics paired with existing knowledge yields a new and powerful tool for condition monitoring. By the use of multivariate techniques and acoustics it is possible to quantify wear and tear as well as predict the performance of working components in complex machinery. This presentation describes the AMCM method and one result of a feasibility study conducted onboard the LPG/C `Norgas Mariner` owned by Norwegian Gas Carriers as (NGC), Oslo. (orig.) 6 refs.
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Stelzer, Robert
Univariate superpositions of Ornstein-Uhlenbeck (OU) type processes, called supOU processes, provide a class of continuous time processes capable of exhibiting long memory behaviour. This paper introduces multivariate supOU processes and gives conditions for their existence and finiteness...... of moments. Moreover, the second order moment structure is explicitly calculated, and examples exhibit the possibility of long range dependence. Our supOU processes are defined via homogeneous and factorisable Lévy bases. We show that the behaviour of supOU processes is particularly nice when the mean...... reversion parameter is restricted to normal matrices and especially to strictly negative definite ones.For finite variation Lévy bases we are able to give conditions for supOU processes to have locally bounded càdlàg paths of finite variation and to show an analogue of the stochastic differential equation...
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Stelzer, Robert
2011-01-01
Univariate superpositions of Ornstein–Uhlenbeck-type processes (OU), called supOU processes, provide a class of continuous time processes capable of exhibiting long memory behavior. This paper introduces multivariate supOU processes and gives conditions for their existence and finiteness of moments....... Moreover, the second-order moment structure is explicitly calculated, and examples exhibit the possibility of long-range dependence. Our supOU processes are defined via homogeneous and factorizable Lévy bases. We show that the behavior of supOU processes is particularly nice when the mean reversion...... parameter is restricted to normal matrices and especially to strictly negative definite ones. For finite variation Lévy bases we are able to give conditions for supOU processes to have locally bounded càdlàg paths of finite variation and to show an analogue of the stochastic differential equation of OU...
An Exact Confidence Region in Multivariate Calibration
Mathew, Thomas; Kasala, Subramanyam
1994-01-01
In the multivariate calibration problem using a multivariate linear model, an exact confidence region is constructed. It is shown that the region is always nonempty and is invariant under nonsingular transformations.
A kernel version of multivariate alteration detection
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack
2013-01-01
Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations.......Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations....
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....
TMVA - Toolkit for Multivariate Data Analysis with ROOT Users guide
Höcker, A; Tegenfeldt, F; Voss, H; Voss, K; Christov, A; Henrot-Versillé, S; Jachowski, M; Krasznahorkay, A; Mahalalel, Y; Prudent, X; Speckmayer, P
2007-01-01
Multivariate machine learning techniques for the classification of data from high-energy physics (HEP) experiments have become standard tools in most HEP analyses. The multivariate classifiers themselves have significantly evolved in recent years, also driven by developments in other areas inside and outside science. TMVA is a toolkit integrated in ROOT which hosts a large variety of multivariate classification algorithms. They range from rectangular cut optimisation (using a genetic algorithm) and likelihood estimators, over linear and non-linear discriminants (neural networks), to sophisticated recent developments like boosted decision trees and rule ensemble fitting. TMVA organises the simultaneous training, testing, and performance evaluation of all these classifiers with a user-friendly interface, and expedites the application of the trained classifiers to the analysis of data sets with unknown sample composition.
Multivariate pluvial flood damage models
International Nuclear Information System (INIS)
Van Ootegem, Luc; Verhofstadt, Elsy; Van Herck, Kristine; Creten, Tom
2015-01-01
Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods. - Highlights: • Prediction of damage of pluvial floods using also non-hazard information • We include ‘no damage cases’ using a Tobit model. • The damage of flood depth is stronger for ground floor than for basement floods. • Non-hazard indicators are especially important for content damage. • Potential gain of policies that increase awareness of flood risks
Multivariate pluvial flood damage models
Energy Technology Data Exchange (ETDEWEB)
Van Ootegem, Luc [HIVA — University of Louvain (Belgium); SHERPPA — Ghent University (Belgium); Verhofstadt, Elsy [SHERPPA — Ghent University (Belgium); Van Herck, Kristine; Creten, Tom [HIVA — University of Louvain (Belgium)
2015-09-15
Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods. - Highlights: • Prediction of damage of pluvial floods using also non-hazard information • We include ‘no damage cases’ using a Tobit model. • The damage of flood depth is stronger for ground floor than for basement floods. • Non-hazard indicators are especially important for content damage. • Potential gain of policies that increase awareness of flood risks.
Multivariate Bioclimatic Ecosystem Change Approaches
2015-02-06
course the sandy soils of the Sandhills will not migrate. This observation suggests that a new nomenclature for ecosystems must be developed if...Coast Sandhills. At that time period, not only will the climate be similar, but the soil character will also be similar. Therefore about the year 2115...Disaggregation of global circulation model outputs decision and policy analysis. Working Paper No. 2. Cali, Colombia : International Centre for Tropical
Multivariate refined composite multiscale entropy analysis
International Nuclear Information System (INIS)
Humeau-Heurtier, Anne
2016-01-01
Multiscale entropy (MSE) has become a prevailing method to quantify signals complexity. MSE relies on sample entropy. However, MSE may yield imprecise complexity estimation at large scales, because sample entropy does not give precise estimation of entropy when short signals are processed. A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. Nevertheless, RCMSE is for univariate signals only. The simultaneous analysis of multi-channel (multivariate) data often over-performs studies based on univariate signals. We therefore introduce an extension of RCMSE to multivariate data. Applications of multivariate RCMSE to simulated processes reveal its better performances over the standard multivariate MSE. - Highlights: • Multiscale entropy quantifies data complexity but may be inaccurate at large scale. • A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. • Nevertheless, RCMSE is adapted to univariate time series only. • We herein introduce an extension of RCMSE to multivariate data. • It shows better performances than the standard multivariate multiscale entropy.
Directory of Open Access Journals (Sweden)
Pedro Silveira Máximo
2009-12-01
Full Text Available O objetivo deste estudo foi, justamente, identificar, entre os métodos LOGIT e a análise multivariada, qual a mais eficaz para estimar a Disposição a Aceitar Compensação (DAC dos cafeicultores quando o viés da utilidade marginal é passível de ocorrência. Para tal, foi elaborado um formulário com 33 perguntas envolvendo informações sobre características socioeconômicas dos cafeicultores, o uso da metodologia de valoração de contingente (MVC e do veículo de pagamento dos "Jogos de Lances", que revelou a Disposição a Aceitar uma Compensação (DAC na troca de um hectare de café por um hectare de mata. Como esperado, por causa do viés da utilidade marginal o método LOGIT foi incapaz de produzir resultados consistentes. Já a estimação da DAC pela análise multivariada mostrou que, caso o governo estivesse disposto a aumentar a provisão de mata em 70 ha, ele deveria despender 254.200 reais por ano, tratando apenas dos cafeicultores vinculados ao programa do PRO-CAFÉ.The object of this study was to identify which method, either LOGIT or multivariate analyses, was the most efficient to estimate the coffee planters' Willingness to Accept a Compensation, when there was a possibility of occurrence of marginal utility. For such, a questionnaire was formulated, with 33 questions involving information on coffee planters' socio - economic characteristics, the use of the methodology of contingent valuation (MCV, and the payment of the "offer game" that reveled the willingness to accept a compensation (WAC, by exchanging a hectare of coffee by a hectare of forest. As expected, because of the marginal utility's bias, the LOGIT method was unable to produce consistent results. However, when the WAC was estimated by multivariate analyses, the results showed that if the government is willing to increase the provision of forest to 70 hectares, it should pay out 254,200 reais (around 116,000 dollars, dealing only with the coffee planters
Multivariate Regression Analysis and Slaughter Livestock,
AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY
A robust multivariate long run analysis of European electricity prices
Bruno Bosco; Lucia Parisio; Matteo Pelagatti; Fabio Baldi
2007-01-01
This paper analyses the interdependencies existing in wholesale electricity prices in six major European countries. The results of our robust multivariate long run dynamic analysis reveal the presence of four highly integrated central European markets (France, Germany, the Netherlands and Austria). The trend shared by these four electricity markets appears to be common also to gas prices, but not to oil prices. The existence of long term dynamics among electricity prices and between electrici...
Paal, Jaanus; Kullman, Bellis; Huijser, Henk A.
1998-01-01
The multivariate structure of the Scutellinia umbrorum complex ( Pezizales, Ascomycetes), based on the morphometrical parameters of 81 specimens from five ecotopes in the Netherlands, was analysed. According to conventional expert estimation, five putative taxa resp. species were established: S.
Multivariate Marshall and Olkin Exponential Minification Process ...
African Journals Online (AJOL)
A stationary bivariate minification process with bivariate Marshall-Olkin exponential distribution that was earlier studied by Miroslav et al [15]is in this paper extended to multivariate minification process with multivariate Marshall and Olkin exponential distribution as its stationary marginal distribution. The innovation and the ...
Multivariate multiscale entropy of financial markets
Lu, Yunfan; Wang, Jun
2017-11-01
In current process of quantifying the dynamical properties of the complex phenomena in financial market system, the multivariate financial time series are widely concerned. In this work, considering the shortcomings and limitations of univariate multiscale entropy in analyzing the multivariate time series, the multivariate multiscale sample entropy (MMSE), which can evaluate the complexity in multiple data channels over different timescales, is applied to quantify the complexity of financial markets. Its effectiveness and advantages have been detected with numerical simulations with two well-known synthetic noise signals. For the first time, the complexity of four generated trivariate return series for each stock trading hour in China stock markets is quantified thanks to the interdisciplinary application of this method. We find that the complexity of trivariate return series in each hour show a significant decreasing trend with the stock trading time progressing. Further, the shuffled multivariate return series and the absolute multivariate return series are also analyzed. As another new attempt, quantifying the complexity of global stock markets (Asia, Europe and America) is carried out by analyzing the multivariate returns from them. Finally we utilize the multivariate multiscale entropy to assess the relative complexity of normalized multivariate return volatility series with different degrees.
A joint model for multivariate hierarchical semicontinuous data with replications.
Kassahun-Yimer, Wondwosen; Albert, Paul S; Lipsky, Leah M; Nansel, Tonja R; Liu, Aiyi
2017-01-01
Longitudinal data are often collected in biomedical applications in such a way that measurements on more than one response are taken from a given subject repeatedly overtime. For some problems, these multiple profiles need to be modeled jointly to get insight on the joint evolution and/or association of these responses over time. In practice, such longitudinal outcomes may have many zeros that need to be accounted for in the analysis. For example, in dietary intake studies, as we focus on in this paper, some food components are eaten daily by almost all subjects, while others are consumed episodically, where individuals have time periods where they do not eat these components followed by periods where they do. These episodically consumed foods need to be adequately modeled to account for the many zeros that are encountered. In this paper, we propose a joint model to analyze multivariate hierarchical semicontinuous data characterized by many zeros and more than one replicate observations at each measurement occasion. This approach allows for different probability mechanisms for describing the zero behavior as compared with the mean intake given that the individual consumes the food. To deal with the potentially large number of multivariate profiles, we use a pairwise model fitting approach that was developed in the context of multivariate Gaussian random effects models with large number of multivariate components. The novelty of the proposed approach is that it incorporates: (1) multivariate, possibly correlated, response variables; (2) within subject correlation resulting from repeated measurements taken from each subject; (3) many zero observations; (4) overdispersion; and (5) replicate measurements at each visit time.
Lyverse, M.A.
1987-01-01
Lithologic data are presented for 113 wells drilled at the Maxey Flats Radioactive Waste Disposal Site for the period June 13, 1984 to December 4, 1986. Water levels, tritium concentrations, and specific conductance are also presented for wells yielding sufficient water for measuring and sampling. At least one sample was collected from most wells for the determination of gross alpha and beta activity. These activities and the results for gamma emitting radionuclides (Cobalt 60 and Cesium 137) are also presented. (USGS)
... ovary syndrome. Read our information on PCOS for teens , and see your doctor if you think you may have PCOS. Major weight loss. Girls who have anorexia will often stop having periods. When to see ...
Multivariate meta-analysis: Potential and promise
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
Exploratory multivariate analysis by example using R
Husson, Francois; Pages, Jerome
2010-01-01
Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the prin
Multivariable control in nuclear power stations
International Nuclear Information System (INIS)
Parent, M.; McMorran, P.D.
1982-11-01
Multivariable methods have the potential to improve the control of large systems such as nuclear power stations. Linear-quadratic optimal control is a multivariable method based on the minimization of a cost function. A related technique leads to the Kalman filter for estimation of plant state from noisy measurements. A design program for optimal control and Kalman filtering has been developed as part of a computer-aided design package for multivariable control systems. The method is demonstrated on a model of a nuclear steam generator, and simulated results are presented
Application of multivariate statistical techniques in microbial ecology.
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.
Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin
zhang, L.
2011-12-01
Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be
Directional outlyingness for multivariate functional data
Dai, Wenlin; Genton, Marc G.
2018-01-01
The direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, classical depth is generalized to directional outlyingness for functional data. Theoretical properties of functional
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...
Multivariate survival analysis and competing risks
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.
Simplicial band depth for multivariate functional data
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
Ellipsoidal prediction regions for multivariate uncertainty characterization
DEFF Research Database (Denmark)
Golestaneh, Faranak; Pinson, Pierre; Azizipanah-Abarghooee, Rasoul
2018-01-01
, for classes of decision-making problems based on robust, interval chance-constrained optimization, necessary inputs take the form of multivariate prediction regions rather than scenarios. The current literature is at very primitive stage of characterizing multivariate prediction regions to be employed...... in these classes of optimization problems. To address this issue, we introduce a new class of multivariate forecasts which form as multivariate ellipsoids for non-Gaussian variables. We propose a data-driven systematic framework to readily generate and evaluate ellipsoidal prediction regions, with predeﬁned...... probability guarantees and minimum conservativeness. A skill score is proposed for quantitative assessment of the quality of prediction ellipsoids. A set of experiments is used to illustrate the discrimination ability of the proposed scoring rule for potential misspeciﬁcation of ellipsoidal prediction regions...
An Introduction to Applied Multivariate Analysis
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.
A multivariate tobit analysis of highway accident-injury-severity rates.
Anastasopoulos, Panagiotis Ch; Shankar, Venky N; Haddock, John E; Mannering, Fred L
2012-03-01
Relatively recent research has illustrated the potential that tobit regression has in studying factors that affect vehicle accident rates (accidents per distance traveled) on specific roadway segments. Tobit regression has been used because accident rates on specific roadway segments are continuous data that are left-censored at zero (they are censored because accidents may not be observed on all roadway segments during the period over which data are collected). This censoring may arise from a number of sources, one of which being the possibility that less severe crashes may be under-reported and thus may be less likely to appear in crash databases. Traditional tobit-regression analyses have dealt with the overall accident rate (all crashes regardless of injury severity), so the issue of censoring by the severity of crashes has not been addressed. However, a tobit-regression approach that considers accident rates by injury-severity level, such as the rate of no-injury, possible injury and injury accidents per distance traveled (as opposed to all accidents regardless of injury-severity), can potentially provide new insights, and address the possibility that censoring may vary by crash-injury severity. Using five-year data from highways in Washington State, this paper estimates a multivariate tobit model of accident-injury-severity rates that addresses the possibility of differential censoring across injury-severity levels, while also accounting for the possible contemporaneous error correlation resulting from commonly shared unobserved characteristics across roadway segments. The empirical results show that the multivariate tobit model outperforms its univariate counterpart, is practically equivalent to the multivariate negative binomial model, and has the potential to provide a fuller understanding of the factors determining accident-injury-severity rates on specific roadway segments. Published by Elsevier Ltd.
Multivariable Feedback Control of Nuclear Reactors
Directory of Open Access Journals (Sweden)
Rune Moen
1982-07-01
Full Text Available Multivariable feedback control has been adapted for optimal control of the spatial power distribution in nuclear reactor cores. Two design techniques, based on the theory of automatic control, were developed: the State Variable Feedback (SVF is an application of the linear optimal control theory, and the Multivariable Frequency Response (MFR is based on a generalization of the traditional frequency response approach to control system design.
Application of multivariate splines to discrete mathematics
Xu, Zhiqiang
2005-01-01
Using methods developed in multivariate splines, we present an explicit formula for discrete truncated powers, which are defined as the number of non-negative integer solutions of linear Diophantine equations. We further use the formula to study some classical problems in discrete mathematics as follows. First, we extend the partition function of integers in number theory. Second, we exploit the relation between the relative volume of convex polytopes and multivariate truncated powers and giv...
Modelling the Covariance Structure in Marginal Multivariate Count Models
DEFF Research Database (Denmark)
Bonat, W. H.; Olivero, J.; Grande-Vega, M.
2017-01-01
The main goal of this article is to present a flexible statistical modelling framework to deal with multivariate count data along with longitudinal and repeated measures structures. The covariance structure for each response variable is defined in terms of a covariance link function combined...... be used to indicate whether there was statistical evidence of a decline in blue duikers and other species hunted during the study period. Determining whether observed drops in the number of animals hunted are indeed true is crucial to assess whether species depletion effects are taking place in exploited...... with a matrix linear predictor involving known matrices. In order to specify the joint covariance matrix for the multivariate response vector, the generalized Kronecker product is employed. We take into account the count nature of the data by means of the power dispersion function associated with the Poisson...
Directory of Open Access Journals (Sweden)
Yuki Furuse
2016-11-01
Full Text Available Infections with the influenza C virus causing respiratory symptoms are common, particularly among children. Since isolation and detection of the virus are rarely performed, compared with influenza A and B viruses, the small number of available sequences of the virus makes it difficult to analyze its evolutionary dynamics. Recently, we reported the full genome sequence of 102 strains of the virus. Here, we exploited the data to elucidate the evolutionary characteristics and phylodynamics of the virus compared with influenza A and B viruses. Along with our data, we obtained public sequence data of the hemagglutinin-esterase gene of the virus; the dataset consists of 218 unique sequences of the virus collected from 14 countries between 1947 and 2014. Informatics analyses revealed that (1 multiple lineages have been circulating globally; (2 there have been weak and infrequent selective bottlenecks; (3 the evolutionary rate is low because of weak positive selection and a low capability to induce mutations; and (4 there is no significant positive selection although a few mutations affecting its antigenicity have been induced. The unique evolutionary dynamics of the influenza C virus must be shaped by multiple factors, including virological, immunological, and epidemiological characteristics.
Energy Technology Data Exchange (ETDEWEB)
Yuankai, L. [Shenzhen Gas Corporation Ltd (China)
2000-07-01
The rapidly improving infrastructure and fast growing economic strengths of cities along the Pearl River in southern China is key to the soaring LPG consumption, and also provided great potential for further development In both LPG and LNG businesses in view of a population of 70 million with a only a 40% gas coverage. The existing local pressurized storage tanks lag behind the demand, but planed and projected refrigerating storage facilities and pressurized storage tanks far exceed the future demands by the next decade. To avoid redundancy and excess density, measures has been proposed of West and East evacuation by way of rivers and reopening of railways to ship LPG from south to North regions. It is advisable to keep in mind the following points in making further investments in LPG facilities: rotation periods should be strictly observed, Full play should be brought to the local storage terminals, Economic sustainability and market saturation should be given full consideration, Complementability of LPG storage facilities and LNG receiving Terminals. (author)
Thomas, Matthew C; Selinger, L Brent; Inglis, G Douglas
2012-08-01
The temporal dynamics of planktonic protists in river water have received limited attention despite their ecological significance and recent studies linking phagotrophic protists to the persistence of human-pathogenic bacteria. Using molecular-based techniques targeting the 18S rRNA gene, we studied the seasonal diversity of planktonic protists in Southwestern Alberta rivers (Oldman River Basin) over a 1-year period. Nonmetric multidimensional scaling analysis of terminal restriction fragment length polymorphism (T-RFLP) data revealed distinct shifts in protistan community profiles that corresponded to season rather than geographical location. Community structures were examined by using clone library analysis; HaeIII restriction profiles of 18S rRNA gene amplicons were used to remove prevalent solanaceous plant clones prior to sequencing. Sanger sequencing of the V1-to-V3 region of the 18S rRNA gene libraries from spring, summer, fall, and winter supported the T-RFLP results and showed marked seasonal differences in the protistan community structure. The spring library was dominated by Chloroplastidae (29.8%), Centrohelida (28.1%), and Alveolata (25.5%), while the summer and fall libraries contained primarily fungal clones (83.0% and 88.0%, respectively). Alveolata (35.6%), Euglenozoa (24.4%), Chloroplastida (15.6%), and Fungi (15.6%) dominated the winter library. These data demonstrate that planktonic protists, including protozoa, are abundant in river water in Southwestern Alberta and that conspicuous seasonal shifts occur in the community structure.
International Nuclear Information System (INIS)
Tapping, C.R.; Scott, P.M.; Lakshminarayan, R.; Ettles, D.F.; Robinson, G.J.
2012-01-01
Aim: To identify variables related to complications following tunnelled dialysis catheter (TDC) replacement and stratifying the risk to reduce morbidity in patients with end-stage renal disease. Materials and methods: One hundred and forty TDCs (Split Cath, medCOMP) were replaced in 140 patients over a 5 year period. Multiple variables were retrospectively collected and analysed to stratify the risk and to predict patients who were more likely to suffer from complications. Multivariate regression analysis was used to identify variables predictive of complications. Results: There were six immediate complications, 42 early complications, and 37 late complications. Multivariate analysis revealed that variables significantly associated to complications were: female sex (p = 0.003; OR 2.9); previous TDC in the same anatomical position in the past (p = 0.014; OR 4.1); catheter exchange (p = 0.038; OR 3.8); haemoglobin 15 s (p = 0.002; OR 4.1); and C-reactive protein >50 mg/l (p = 0.007; OR 4.6). A high-risk score, which used the values from the multivariate analysis, predicted 100% of the immediate complications, 95% of the early complications, and 68% of the late complications. Conclusion: Patients can now be scored prior to TDC replacement. A patient with a high-risk score can be optimized to reduce the chance of complications. Further prospective studies to confirm that rotating the site of TDC reduces complications are warranted as this has implications for current guidelines.
A multivariate time series approach to modeling and forecasting demand in the emergency department.
Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L
2009-02-01
The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.
Multivariate Max-Stable Spatial Processes
Genton, Marc G.
2014-01-06
Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.
Multivariate Max-Stable Spatial Processes
Genton, Marc G.
2014-01-01
Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.
Preference learning with evolutionary Multivariate Adaptive Regression Spline model
DEFF Research Database (Denmark)
Abou-Zleikha, Mohamed; Shaker, Noor; Christensen, Mads Græsbøll
2015-01-01
This paper introduces a novel approach for pairwise preference learning through combining an evolutionary method with Multivariate Adaptive Regression Spline (MARS). Collecting users' feedback through pairwise preferences is recommended over other ranking approaches as this method is more appealing...... for function approximation as well as being relatively easy to interpret. MARS models are evolved based on their efficiency in learning pairwise data. The method is tested on two datasets that collectively provide pairwise preference data of five cognitive states expressed by users. The method is analysed...
An architecture for implementation of multivariable controllers
DEFF Research Database (Denmark)
Niemann, Hans Henrik; Stoustrup, Jakob
1999-01-01
Browse > Conferences> American Control Conference, Prev | Back to Results | Next » An architecture for implementation of multivariable controllers 786292 searchabstract Niemann, H. ; Stoustrup, J. ; Dept. of Autom., Tech. Univ., Lyngby This paper appears in: American Control Conference, 1999....... Proceedings of the 1999 Issue Date : 1999 Volume : 6 On page(s): 4029 - 4033 vol.6 Location: San Diego, CA Meeting Date : 02 Jun 1999-04 Jun 1999 Print ISBN: 0-7803-4990-3 References Cited: 7 INSPEC Accession Number: 6403075 Digital Object Identifier : 10.1109/ACC.1999.786292 Date of Current Version : 06...... august 2002 Abstract An architecture for implementation of multivariable controllers is presented in this paper. The architecture is based on the Youla-Jabr-Bongiorno-Kucera parameterization of all stabilizing controllers. By using this architecture for implementation of multivariable controllers...
A MULTIVARIATE ANALYSIS OF CROATIAN COUNTIES ENTREPRENEURSHIP
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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.
Simplicial band depth for multivariate functional data
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.
Multivariate generalized linear mixed models using R
Berridge, Damon Mark
2011-01-01
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model...
A MATLAB companion for multivariable calculus
Cooper, Jeffery
2001-01-01
Offering a concise collection of MatLab programs and exercises to accompany a third semester course in multivariable calculus, A MatLab Companion for Multivariable Calculus introduces simple numerical procedures such as numerical differentiation, numerical integration and Newton''s method in several variables, thereby allowing students to tackle realistic problems. The many examples show students how to use MatLab effectively and easily in many contexts. Numerous exercises in mathematics and applications areas are presented, graded from routine to more demanding projects requiring some programming. Matlab M-files are provided on the Harcourt/Academic Press web site at http://www.harcourt-ap.com/matlab.html.* Computer-oriented material that complements the essential topics in multivariable calculus* Main ideas presented with examples of computations and graphics displays using MATLAB * Numerous examples of short code in the text, which can be modified for use with the exercises* MATLAB files are used to implem...
Multivariate Analysis and Prediction of Dioxin-Furan ...
Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE
Multivariable nonlinear analysis of foreign exchange rates
Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo
2003-05-01
We analyze the multivariable time series of foreign exchange rates. These are price movements that have often been analyzed, and dealing time intervals and spreads between bid and ask prices. Considering dealing time intervals as event timing such as neurons’ firings, we use raster plots (RPs) and peri-stimulus time histograms (PSTHs) which are popular methods in the field of neurophysiology. Introducing special processings to obtaining RPs and PSTHs time histograms for analyzing exchange rates time series, we discover that there exists dynamical interaction among three variables. We also find that adopting multivariables leads to improvements of prediction accuracy.
Collision prediction models using multivariate Poisson-lognormal regression.
El-Basyouny, Karim; Sayed, Tarek
2009-07-01
This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate Poisson-lognormal (PLN) models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency. The MVPLN is modeled using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a goodness-of-fit measure for model comparisons. The results indicate that the estimates of the extra Poisson variation parameters were considerably smaller under MVPLN leading to higher precision. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables representing property damage only (PDO) and injuries plus fatalities (I+F). This correlation was estimated at 0.758, which is highly significant, suggesting that higher PDO rates are associated with higher I+F rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in roadway design and/or other unobserved factors. In terms of goodness-of-fit, the MVPLN model provided a superior fit than the independent univariate models. The multivariate hazardous location identification results demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models.
Calculus of multivariate functions: it's application in business | Awen ...
African Journals Online (AJOL)
Multivariate functions can be applied to situations in business organizations like ... of capital invested in the plant, the size of the labour force and the cost of raw ... of multivariate functions and has considered types of multivariate differentiation ...
Multivariate Analysis of Industrial Scale Fermentation Data
DEFF Research Database (Denmark)
Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart M.
2015-01-01
Multivariate analysis allows process understanding to be gained from the vast and complex datasets recorded from fermentation processes, however the application of such techniques to this field can be limited by the data pre-processing requirements and data handling. In this work many iterations...
Multivariate Option Pricing Using Dynamic Copula Models
van den Goorbergh, R.W.J.; Genest, C.; Werker, B.J.M.
2003-01-01
This paper examines the behavior of multivariate option prices in the presence of association between the underlying assets.Parametric families of copulas offering various alternatives to the normal dependence structure are used to model this association, which is explicitly assumed to vary over
Fully conditional specification in multivariate imputation
van Buuren, S.; Brand, J. P.L.; Groothuis-Oudshoorn, C. G.M.; Rubin, D. B.
2006-01-01
The use of the Gibbs sampler with fully conditionally specified models, where the distribution of each variable given the other variables is the starting point, has become a popular method to create imputations in incomplete multivariate data. The theoretical weakness of this approach is that the
Multivariate ordination statistics workshop with R slides
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).
Multivariate Analysis of Schools and Educational Policy.
Kiesling, Herbert J.
This report describes a multivariate analysis technique that approaches the problems of educational production function analysis by (1) using comparable measures of output across large experiments, (2) accounting systematically for differences in socioeconomic background, and (3) treating the school as a complete system in which different…
Multivariate Discrete First Order Stochastic Dominance
DEFF Research Database (Denmark)
Tarp, Finn; Østerdal, Lars Peter
This paper characterizes the principle of first order stochastic dominance in a multivariate discrete setting. We show that a distribution f first order stochastic dominates distribution g if and only if f can be obtained from g by iteratively shifting density from one outcome to another...
Multivariate Time Series Decomposition into Oscillation Components.
Matsuda, Takeru; Komaki, Fumiyasu
2017-08-01
Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.
Ranking multivariate GARCH models by problem dimension
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
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...
Advanced multivariate analysis to assess remediation of hydrocarbons in soils.
Lin, Deborah S; Taylor, Peter; Tibbett, Mark
2014-10-01
Accurate monitoring of degradation levels in soils is essential in order to understand and achieve complete degradation of petroleum hydrocarbons in contaminated soils. We aimed to develop the use of multivariate methods for the monitoring of biodegradation of diesel in soils and to determine if diesel contaminated soils could be remediated to a chemical composition similar to that of an uncontaminated soil. An incubation experiment was set up with three contrasting soil types. Each soil was exposed to diesel at varying stages of degradation and then analysed for key hydrocarbons throughout 161 days of incubation. Hydrocarbon distributions were analysed by Principal Coordinate Analysis and similar samples grouped by cluster analysis. Variation and differences between samples were determined using permutational multivariate analysis of variance. It was found that all soils followed trajectories approaching the chemical composition of the unpolluted soil. Some contaminated soils were no longer significantly different to that of uncontaminated soil after 161 days of incubation. The use of cluster analysis allows the assignment of a percentage chemical similarity of a diesel contaminated soil to an uncontaminated soil sample. This will aid in the monitoring of hydrocarbon contaminated sites and the establishment of potential endpoints for successful remediation.
Assesment of winter wheat advanced lines by use of multivariate statistical analyses
Directory of Open Access Journals (Sweden)
Boshev Dane
2016-01-01
Full Text Available This study was conducted to evaluate 49 advanced lines of winter wheat (Triticum aestivum L. for their morphoagronomic traits and to determine best criteria for selection of lines to be included in future breeding program. The material was assessed in two years experiment at two locations, using RCBD design with three replications. Ten quantitative traits: plant height, number of fertile tillers, spike length, number of spikelets per spike, number of grains per spike, weight of grain per spike and per plant, fertility, biological yield and harvest index were evaluated by PCA and two-way cluster analysis. Three main principal components were determined explaining 71.391% of the total variation among the genotypes. One third of the variation is explained by PC1 which reflects the genotype yield potential. PC2 and PC3 explained 25.22% and 15.49% of the total variance, mostly in relation to the plant height and spike components, respectively. Biplot graph revealed strongest positive association between spike length, number of spikelets and biological yield and between number of tillers, weight of grains per spike and per plant. Two-way cluster analysis resulted with a dendrogram with one solely separated genotype, superior for all traits and two main clusters of genotypes defined with wide genetic diversity especially between the groups within the second cluster. Genotypes with high values for specific traits will be included in the future breeding programmes. Classification of genotypes and the extend of variation among them illustrated on the heatmap has proved to be practical tool for selecting genotypes with desired traits in the early stages of the breeding process.
Multivariate analyses of CT findings in typical schizophrenia and atypical psychosis
International Nuclear Information System (INIS)
Hayashi, Takuji; Watanabe, Toyonobu; Kitoh, Hiroshi; Sekine, Takeo
1992-01-01
In order to investigate the brain morphological differences between typical schizophrenia and atypical psychosis, the brain CTs of 41 patients with typical schizophrenia, 27 patients with atypical psychosis (ATP), and 20 controls were examined. The schizophrenics had larger values for 9 CT indices, i.e., interhemispheric fissure (IHF) index, VBR, 2 lateral ventricles (L-V) and 3rd venricle (III-V) indices, and 4 sylvian fissure (SF) indices, while the values of ATP patients for 3 SF indices were greater than for the controls. Moreover, the schizophrenics had greater III-V and L-V indices than the ATP patients. The correlation matrix of CT indices indicates that the III-V index correlated well with the other CT indices, whereas the VBR, IHF and right SF indices did not. Therefore, it was speculated that there might be 3 subgroups, each of which has a main focus of alteration in the above-mentioned regions. Therefore, all the cases were divided by means of a cluster analysis into 5 groups. Group I, which contained mainly normal controls, and Group II, which consisted mainly of atypical psychosis patients, had no abnormal CT findings. Group III, which comprised mainly ATP pateints and paranoid type schizophrenics, had right SF enlargement. Group IV, which showed significant IHF enlargement, and the residue group, which had larger VBR and significant left SF enlargement, consisted mostly of schizophrenics. Thus, our results suggest that the classification by CT data corresponds on the whole to our clinical diagnosis, according to which schizophrenic psychosis is divided into typical schizophrenia and atypical psychosis, and that each of the two psychosis groups may be further classified into distinct subgroups. (author)
Utilisation des analyses multivariées dans la caractérisation de la ...
African Journals Online (AJOL)
SARAH
30 juin 2013 ... sélectivité des filets de pêche dépend de la nature du filet, de la maille, de la hauteur de chute, du rapport d'armement, de la saison et de la longueur totale du poisson et que d'autre part les FMDS à grandes mailles. (56 à 60mm) sont les plus sélectifs de tous les engins utilisés pour la capture du mulet ...
Multivariate analyses of CT findings in typical schizophrenia and atypical psychosis
Energy Technology Data Exchange (ETDEWEB)
Hayashi, Takuji; Watanabe, Toyonobu; Kitoh, Hiroshi; Sekine, Takeo (Aichi Medical Univ., Nagakute (Japan))
1992-09-01
In order to investigate the brain morphological differences between typical schizophrenia and atypical psychosis, the brain CTs of 41 patients with typical schizophrenia, 27 patients with atypical psychosis (ATP), and 20 controls were examined. The schizophrenics had larger values for 9 CT indices, i.e., interhemispheric fissure (IHF) index, VBR, 2 lateral ventricles (L-V) and 3rd venricle (III-V) indices, and 4 sylvian fissure (SF) indices, while the values of ATP patients for 3 SF indices were greater than for the controls. Moreover, the schizophrenics had greater III-V and L-V indices than the ATP patients. The correlation matrix of CT indices indicates that the III-V index correlated well with the other CT indices, whereas the VBR, IHF and right SF indices did not. Therefore, it was speculated that there might be 3 subgroups, each of which has a main focus of alteration in the above-mentioned regions. Therefore, all the cases were divided by means of a cluster analysis into 5 groups. Group I, which contained mainly normal controls, and Group II, which consisted mainly of atypical psychosis patients, had no abnormal CT findings. Group III, which comprised mainly ATP pateints and paranoid type schizophrenics, had right SF enlargement. Group IV, which showed significant IHF enlargement, and the residue group, which had larger VBR and significant left SF enlargement, consisted mostly of schizophrenics. Thus, our results suggest that the classification by CT data corresponds on the whole to our clinical diagnosis, according to which schizophrenic psychosis is divided into typical schizophrenia and atypical psychosis, and that each of the two psychosis groups may be further classified into distinct subgroups. (author).
Multivariate analyses of Burkholderia species in soil: effect of crop and land use history
Salles, Joanna; Van Veen, J.A.; van Elsas, J.D.
2004-01-01
The assessment of Burkholderia diversity in agricultural areas is important considering the potential use of this genus for agronomic and environmental applications. Therefore, the aim of this work was to ascertain how plant species and land use management drive the diversity of the genus
Multivariate analyses of Burkholderia species in soil : Effect of crop and land use history
Salles, JF; van Veen, JA; van Elsas, JD
The assessment of Burkholderia diversity in agricultural areas is important considering the potential use of this genus for agronomic and environmental applications. Therefore, the aim of this work was to ascertain how plant species and land use management drive the diversity of the genus
Multivariate Analyses of Burkholderia species in soil: effect of crop and land use history.
Salles, J.F.; Veen, van J.A.; Elsas, van J.D.
2004-01-01
The assessment of Burkholderia diversity in agricultural areas is important considering the potential use of this genus for agronomic and environmental applications. Therefore, the aim of this work was to ascertain how plant species and land use management drive the diversity of the genus
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.
Directional outlyingness for multivariate functional data
Dai, Wenlin
2018-04-07
The direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, classical depth is generalized to directional outlyingness for functional data. Theoretical properties of functional directional outlyingness are investigated and the total outlyingness can be naturally decomposed into two parts: magnitude outlyingness and shape outlyingness which represent the centrality of a curve for magnitude and shape, respectively. This decomposition serves as a visualization tool for the centrality of curves. Furthermore, an outlier detection procedure is proposed based on functional directional outlyingness. This criterion applies to both univariate and multivariate curves and simulation studies show that it outperforms competing methods. Weather and electrocardiogram data demonstrate the practical application of our proposed framework.
Multivariate max-stable spatial processes
Genton, Marc G.; Padoan, S. A.; Sang, H.
2015-01-01
Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.
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....
Multivariate max-stable spatial processes
Genton, Marc G.
2015-02-11
Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.
Regression Models For Multivariate Count Data.
Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei
2017-01-01
Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.
A short note on multivariate dependence modeling
Czech Academy of Sciences Publication Activity Database
Bína, V.; Jiroušek, Radim
2013-01-01
Roč. 49, č. 3 (2013), s. 420-432 ISSN 0023-5954 Grant - others:GA ČR(CZ) GAP403/12/2175 Program:GA Institutional support: RVO:67985556 Keywords : multivariate distribution * dependence * copula Subject RIV: IN - Informatics, Computer Science Impact factor: 0.563, year: 2013 http://library.utia.cas.cz/separaty/2014/MTR/jirousek-0427848.pdf
Multivariate Welch t-test on distances
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...
Multivariate fractional Poisson processes and compound sums
Beghin, Luisa; Macci, Claudio
2015-01-01
In this paper we present multivariate space-time fractional Poisson processes by considering common random time-changes of a (finite-dimensional) vector of independent classical (non-fractional) Poisson processes. In some cases we also consider compound processes. We obtain some equations in terms of some suitable fractional derivatives and fractional difference operators, which provides the extension of known equations for the univariate processes.
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
Precision Index in the Multivariate Context
Czech Academy of Sciences Publication Activity Database
Šiman, Miroslav
2014-01-01
Roč. 43, č. 2 (2014), s. 377-387 ISSN 0361-0926 R&D Projects: GA MŠk(CZ) 1M06047 Institutional support: RVO:67985556 Keywords : data depth * multivariate quantile * process capability index * precision index * regression quantile Subject RIV: BA - General Mathematics Impact factor: 0.274, year: 2014 http://library.utia.cas.cz/separaty/2014/SI/siman-0425059.pdf
The evolution of multivariate maternal effects.
Directory of Open Access Journals (Sweden)
Bram Kuijper
2014-04-01
Full Text Available There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.
The evolution of multivariate maternal effects.
Kuijper, Bram; Johnstone, Rufus A; Townley, Stuart
2014-04-01
There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.
Geometric noise reduction for multivariate time series.
Mera, M Eugenia; Morán, Manuel
2006-03-01
We propose an algorithm for the reduction of observational noise in chaotic multivariate time series. The algorithm is based on a maximum likelihood criterion, and its goal is to reduce the mean distance of the points of the cleaned time series to the attractor. We give evidence of the convergence of the empirical measure associated with the cleaned time series to the underlying invariant measure, implying the possibility to predict the long run behavior of the true dynamics.
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
Preliminary Multivariable Cost Model for Space Telescopes
Stahl, H. Philip
2010-01-01
Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
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...
AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION
Krzyśko, Mirosław; Smaga, Łukasz
2017-01-01
In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...
Potyrailo, Radislav A
2017-08-29
For detection of gases and vapors in complex backgrounds, "classic" analytical instruments are an unavoidable alternative to existing sensors. Recently a new generation of sensors, known as multivariable sensors, emerged with a fundamentally different perspective for sensing to eliminate limitations of existing sensors. In multivariable sensors, a sensing material is designed to have diverse responses to different gases and vapors and is coupled to a multivariable transducer that provides independent outputs to recognize these diverse responses. Data analytics tools provide rejection of interferences and multi-analyte quantitation. This review critically analyses advances of multivariable sensors based on ligand-functionalized metal nanoparticles also known as monolayer-protected nanoparticles (MPNs). These MPN sensing materials distinctively stand out from other sensing materials for multivariable sensors due to their diversity of gas- and vapor-response mechanisms as provided by organic and biological ligands, applicability of these sensing materials for broad classes of gas-phase compounds such as condensable vapors and non-condensable gases, and for several principles of signal transduction in multivariable sensors that result in non-resonant and resonant electrical sensors as well as material- and structure-based photonic sensors. Such features should allow MPN multivariable sensors to be an attractive high value addition to existing analytical instrumentation.
Jia, Xiaoliang; An, Haizhong; Sun, Xiaoqi; Huang, Xuan; Gao, Xiangyun
2016-04-01
The globalization and regionalization of crude oil trade inevitably give rise to the difference of crude oil prices. The understanding of the pattern of the crude oil prices' mutual propagation is essential for analyzing the development of global oil trade. Previous research has focused mainly on the fuzzy long- or short-term one-to-one propagation of bivariate oil prices, generally ignoring various patterns of periodical multivariate propagation. This study presents a wavelet-based network approach to help uncover the multipath propagation of multivariable crude oil prices in a joint time-frequency period. The weekly oil spot prices of the OPEC member states from June 1999 to March 2011 are adopted as the sample data. First, we used wavelet analysis to find different subseries based on an optimal decomposing scale to describe the periodical feature of the original oil price time series. Second, a complex network model was constructed based on an optimal threshold selection to describe the structural feature of multivariable oil prices. Third, Bayesian network analysis (BNA) was conducted to find the probability causal relationship based on periodical structural features to describe the various patterns of periodical multivariable propagation. Finally, the significance of the leading and intermediary oil prices is discussed. These findings are beneficial for the implementation of periodical target-oriented pricing policies and investment strategies.
Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.
Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong
2015-05-01
In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.
Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K
2017-01-01
The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.
Periodic feedback stabilization for linear periodic evolution equations
Wang, Gengsheng
2016-01-01
This book introduces a number of recent advances regarding periodic feedback stabilization for linear and time periodic evolution equations. First, it presents selected connections between linear quadratic optimal control theory and feedback stabilization theory for linear periodic evolution equations. Secondly, it identifies several criteria for the periodic feedback stabilization from the perspective of geometry, algebra and analyses respectively. Next, it describes several ways to design periodic feedback laws. Lastly, the book introduces readers to key methods for designing the control machines. Given its coverage and scope, it offers a helpful guide for graduate students and researchers in the areas of control theory and applied mathematics.
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
Hearing loss among older construction workers: Updated analyses.
Dement, John; Welch, Laura S; Ringen, Knut; Cranford, Kim; Quinn, Patricia
2018-04-01
A prior study of this construction worker population found significant noise-associated hearing loss. This follow-up study included a much larger study population and consideration of additional risk factors. Data included audiometry, clinical chemistry, personal history, and work history. Qualitative exposure metrics for noise and solvents were developed. Analyses compared construction workers to an internal reference group with lower exposures and an external worker population with low noise exposure. Among participants (n = 19 127) an overall prevalence of hearing loss of 58% was observed, with significantly increased prevalence across all construction trades. Construction workers had significantly increased risk of hearing loss compared to reference populations, with increasing risk by work duration. Noise exposure, solvent exposure, hypertension, and smoking were significant risk factors in multivariate models. Results support a causal relationship between construction trades work and hearing loss. Prevention should focus on reducing exposure to noise, solvents, and cigarette smoke. © 2018 Wiley Periodicals, Inc.
The pathways for intelligible speech: multivariate and univariate perspectives.
Evans, S; Kyong, J S; Rosen, S; Golestani, N; Warren, J E; McGettigan, C; Mourão-Miranda, J; Wise, R J S; Scott, S K
2014-09-01
An anterior pathway, concerned with extracting meaning from sound, has been identified in nonhuman primates. An analogous pathway has been suggested in humans, but controversy exists concerning the degree of lateralization and the precise location where responses to intelligible speech emerge. We have demonstrated that the left anterior superior temporal sulcus (STS) responds preferentially to intelligible speech (Scott SK, Blank CC, Rosen S, Wise RJS. 2000. Identification of a pathway for intelligible speech in the left temporal lobe. Brain. 123:2400-2406.). A functional magnetic resonance imaging study in Cerebral Cortex used equivalent stimuli and univariate and multivariate analyses to argue for the greater importance of bilateral posterior when compared with the left anterior STS in responding to intelligible speech (Okada K, Rong F, Venezia J, Matchin W, Hsieh IH, Saberi K, Serences JT,Hickok G. 2010. Hierarchical organization of human auditory cortex: evidence from acoustic invariance in the response to intelligible speech. 20: 2486-2495.). Here, we also replicate our original study, demonstrating that the left anterior STS exhibits the strongest univariate response and, in decoding using the bilateral temporal cortex, contains the most informative voxels showing an increased response to intelligible speech. In contrast, in classifications using local "searchlights" and a whole brain analysis, we find greater classification accuracy in posterior rather than anterior temporal regions. Thus, we show that the precise nature of the multivariate analysis used will emphasize different response profiles associated with complex sound to speech processing. © The Author 2013. Published by Oxford University Press.
Time varying, multivariate volume data reduction
Energy Technology Data Exchange (ETDEWEB)
Ahrens, James P [Los Alamos National Laboratory; Fout, Nathaniel [UC DAVIS; Ma, Kwan - Liu [UC DAVIS
2010-01-01
Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multivariate, and volumetric, can occupy from hundreds of gigabytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering. In this paper we present a solution which addresses the need for data reduction in large supercomputing environments where data resulting from simulations occupies tremendous amounts of storage. Our solution employs a lossy encoding scheme to acrueve data reduction with several options in terms of rate-distortion behavior. We focus on encoding of multiple variables together, with optional compression in space and time. The compressed volumes can be rendered directly with commodity graphics cards at interactive frame rates and rendering quality similar to that of static volume renderers. Compression results using a multivariate time-varying data set indicate that encoding multiple variables results in acceptable performance in the case of spatial and temporal encoding as compared to independent compression of variables. The relative performance of spatial vs. temporal compression is data dependent, although temporal compression has the
Multivariate linear models and repeated measurements revisited
DEFF Research Database (Denmark)
Dalgaard, Peter
2009-01-01
Methods for generalized analysis of variance based on multivariate normal theory have been known for many years. In a repeated measurements context, it is most often of interest to consider transformed responses, typically within-subject contrasts or averages. Efficiency considerations leads...... to sphericity assumptions, use of F tests and the Greenhouse-Geisser and Huynh-Feldt adjustments to compensate for deviations from sphericity. During a recent implementation of such methods in the R language, the general structure of such transformations was reconsidered, leading to a flexible specification...
New multivariable capabilities of the INCA program
Bauer, Frank H.; Downing, John P.; Thorpe, Christopher J.
1989-01-01
The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.
Multivariate Analysis for the Processing of Signals
Directory of Open Access Journals (Sweden)
Beattie J.R.
2014-01-01
Full Text Available Real-world experiments are becoming increasingly more complex, needing techniques capable of tracking this complexity. Signal based measurements are often used to capture this complexity, where a signal is a record of a sample’s response to a parameter (e.g. time, displacement, voltage, wavelength that is varied over a range of values. In signals the responses at each value of the varied parameter are related to each other, depending on the composition or state sample being measured. Since signals contain multiple information points, they have rich information content but are generally complex to comprehend. Multivariate Analysis (MA has profoundly transformed their analysis by allowing gross simplification of the tangled web of variation. In addition MA has also provided the advantage of being much more robust to the influence of noise than univariate methods of analysis. In recent years, there has been a growing awareness that the nature of the multivariate methods allows exploitation of its benefits for purposes other than data analysis, such as pre-processing of signals with the aim of eliminating irrelevant variations prior to analysis of the signal of interest. It has been shown that exploiting multivariate data reduction in an appropriate way can allow high fidelity denoising (removal of irreproducible non-signals, consistent and reproducible noise-insensitive correction of baseline distortions (removal of reproducible non-signals, accurate elimination of interfering signals (removal of reproducible but unwanted signals and the standardisation of signal amplitude fluctuations. At present, the field is relatively small but the possibilities for much wider application are considerable. Where signal properties are suitable for MA (such as the signal being stationary along the x-axis, these signal based corrections have the potential to be highly reproducible, and highly adaptable and are applicable in situations where the data is noisy or
Multivariable dynamic calculus on time scales
Bohner, Martin
2016-01-01
This book offers the reader an overview of recent developments of multivariable dynamic calculus on time scales, taking readers beyond the traditional calculus texts. Covering topics from parameter-dependent integrals to partial differentiation on time scales, the book’s nine pedagogically oriented chapters provide a pathway to this active area of research that will appeal to students and researchers in mathematics and the physical sciences. The authors present a clear and well-organized treatment of the concept behind the mathematics and solution techniques, including many practical examples and exercises.
Multivariable adaptive control of bio process
Energy Technology Data Exchange (ETDEWEB)
Maher, M.; Bahhou, B.; Roux, G. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Maher, M. [Faculte des Sciences, Rabat (Morocco). Lab. de Physique
1995-12-31
This paper presents a multivariable adaptive control of a continuous-flow fermentation process for the alcohol production. The linear quadratic control strategy is used for the regulation of substrate and ethanol concentrations in the bioreactor. The control inputs are the dilution rate and the influent substrate concentration. A robust identification algorithm is used for the on-line estimation of linear MIMO model`s parameters. Experimental results of a pilot-plant fermenter application are reported and show the control performances. (authors) 8 refs.
Topics in multivariate approximation and interpolation
Jetter, Kurt
2005-01-01
This book is a collection of eleven articles, written by leading experts and dealing with special topics in Multivariate Approximation and Interpolation. The material discussed here has far-reaching applications in many areas of Applied Mathematics, such as in Computer Aided Geometric Design, in Mathematical Modelling, in Signal and Image Processing and in Machine Learning, to mention a few. The book aims at giving a comprehensive information leading the reader from the fundamental notions and results of each field to the forefront of research. It is an ideal and up-to-date introduction for gr
Particulate characterization by PIXE multivariate spectral analysis
International Nuclear Information System (INIS)
Antolak, Arlyn J.; Morse, Daniel H.; Grant, Patrick G.; Kotula, Paul G.; Doyle, Barney L.; Richardson, Charles B.
2007-01-01
Obtaining particulate compositional maps from scanned PIXE (proton-induced X-ray emission) measurements is extremely difficult due to the complexity of analyzing spectroscopic data collected with low signal-to-noise at each scan point (pixel). Multivariate spectral analysis has the potential to analyze such data sets by reducing the PIXE data to a limited number of physically realizable and easily interpretable components (that include both spectral and image information). We have adapted the AXSIA (automated expert spectral image analysis) program, originally developed by Sandia National Laboratories to quantify electron-excited X-ray spectroscopy data, for this purpose. Samples consisting of particulates with known compositions and sizes were loaded onto Mylar and paper filter substrates and analyzed by scanned micro-PIXE. The data sets were processed by AXSIA and the associated principal component spectral data were quantified by converting the weighting images into concentration maps. The results indicate automated, nonbiased, multivariate statistical analysis is useful for converting very large amounts of data into a smaller, more manageable number of compositional components needed for locating individual particles-of-interest on large area collection media
Crane cabins' interior space multivariate anthropometric modeling.
Essdai, Ahmed; Spasojević Brkić, Vesna K; Golubović, Tamara; Brkić, Aleksandar; Popović, Vladimir
2018-01-01
Previous research has shown that today's crane cabins fail to meet the needs of a large proportion of operators. Performance and financial losses and effects on safety should not be overlooked as well. The first aim of this survey is to model the crane cabin interior space using up-to-date crane operator anthropometric data and to compare the multivariate and univariate method anthropometric models. The second aim of the paper is to define the crane cabin interior space dimensions that enable anthropometric convenience. To facilitate the cabin design, the anthropometric dimensions of 64 crane operators in the first sample and 19 more in the second sample were collected in Serbia. The multivariate anthropometric models, spanning 95% of the population on the basis of a set of 8 anthropometric dimensions, have been developed. The percentile method was also used on the same set of data. The dimensions of the interior space, necessary for the accommodation of the crane operator, are 1174×1080×1865 mm. The percentiles results for the 5th and 95th model are within the obtained dimensions. The results of this study may prove useful to crane cabin designers in eliminating anthropometric inconsistencies and improving the health of operators, but can also aid in improving the safety, performance and financial results of the companies where crane cabins operate.
MULTIVARIATE ACCOUNTING IN INTERNATIONAL FINANCIAL REPORTING STANDARDS
Directory of Open Access Journals (Sweden)
V. V. IEVDOKYMOV
2017-03-01
Full Text Available The necessity of the research on the basis of the positivist model of scientific knowledge is proved. The value of the conceptual framework in the process of bookkeeping selection is analyzed. The differences of the accounting selection adjustment procedure in US GAAP and IFRS are considered. The role and importance of the qualitative characteristics of financial reporting in the implementation of accounting selection are substantiated. The structure of the qualitative characteristics of financial reporting and their limitations under the Conceptual Framework for the preparation and presentation of financial statements are examined. The correlation between the accounting rules and alternatives adopted in US GAAP and IAS / IFRS is analyzed. The necessity to discuss the issue of the feasibility of «rule-oriented» or «principle-oriented» accounting model in the context of multivariate concept is studied. The authors prove the necessity of the application of institutional theory to solve the problems of accounting opportunism that arises when using the concept of multivariate accounting in International Financial Reporting Standards.
Fuzzy multivariable control of domestic heat pumps
International Nuclear Information System (INIS)
Underwood, C.P.
2015-01-01
Poor control has been identified as one of the reasons why recent field trials of domestic heat pumps in the UK have produced disappointing results. Most of the technology in use today uses a thermostatically-controlled fixed speed compressor with a mechanical expansion device. This article investigates improved control of these heat pumps through the design and evaluation of a new multivariable fuzzy logic control system utilising a variable speed compressor drive with capacity control linked through to evaporator superheat control. A new dynamic thermal model of a domestic heat pump validated using experimental data forms the basis of the work. The proposed control system is evaluated using median and extreme daily heating demand profiles for a typical UK house compared with a basic thermostatically-controlled alternative. Results show good tracking of the heating temperature and superheat control variables, reduced cycling and an improvement in performance averaging 20%. - Highlights: • A new dynamic model of a domestic heat pump is developed and validated. • A new multivariable fuzzy logic heat pump control system is developed/reported. • The fuzzy controller regulates both plant capacity and evaporator superheat degree. • Thermal buffer storage is also considered as well as compressor cycling. • The new controller shows good variable tracking and a reduction in energy of 20%.
Boosted Multivariate Trees for Longitudinal Data
Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant
2017-01-01
Machine learning methods provide a powerful approach for analyzing longitudinal data in which repeated measurements are observed for a subject over time. We boost multivariate trees to fit a novel flexible semi-nonparametric marginal model for longitudinal data. In this model, features are assumed to be nonparametric, while feature-time interactions are modeled semi-nonparametrically utilizing P-splines with estimated smoothing parameter. In order to avoid overfitting, we describe a relatively simple in sample cross-validation method which can be used to estimate the optimal boosting iteration and which has the surprising added benefit of stabilizing certain parameter estimates. Our new multivariate tree boosting method is shown to be highly flexible, robust to covariance misspecification and unbalanced designs, and resistant to overfitting in high dimensions. Feature selection can be used to identify important features and feature-time interactions. An application to longitudinal data of forced 1-second lung expiratory volume (FEV1) for lung transplant patients identifies an important feature-time interaction and illustrates the ease with which our method can find complex relationships in longitudinal data. PMID:29249866
Estimating uncertainty in multivariate responses to selection.
Stinchcombe, John R; Simonsen, Anna K; Blows, Mark W
2014-04-01
Predicting the responses to natural selection is one of the key goals of evolutionary biology. Two of the challenges in fulfilling this goal have been the realization that many estimates of natural selection might be highly biased by environmentally induced covariances between traits and fitness, and that many estimated responses to selection do not incorporate or report uncertainty in the estimates. Here we describe the application of a framework that blends the merits of the Robertson-Price Identity approach and the multivariate breeder's equation to address these challenges. The approach allows genetic covariance matrices, selection differentials, selection gradients, and responses to selection to be estimated without environmentally induced bias, direct and indirect selection and responses to selection to be distinguished, and if implemented in a Bayesian-MCMC framework, statistically robust estimates of uncertainty on all of these parameters to be made. We illustrate our approach with a worked example of previously published data. More generally, we suggest that applying both the Robertson-Price Identity and the multivariate breeder's equation will facilitate hypothesis testing about natural selection, genetic constraints, and evolutionary responses. © 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.
Eley, Thalia C.; Rijsdijk, Fruhling V.; Perrin, Sean; O'Connor, Thomas G.; Bolton, Derek
2008-01-01
Background: Comorbidity amongst anxiety disorders is very common in children as in adults and leads to considerable distress and impairment, yet is poorly understood. Multivariate genetic analyses can shed light on the origins of this comorbidity by revealing whether genetic or environmental risks for one disorder also influence another. We…
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...
Richard. D. Wood-Smith; John M. Buffington
1996-01-01
Multivariate statistical analyses of geomorphic variables from 23 forest stream reaches in southeast Alaska result in successful discrimination between pristine streams and those disturbed by land management, specifically timber harvesting and associated road building. Results of discriminant function analysis indicate that a three-variable model discriminates 10...
MULTIVARIATE MODEL FOR CORPORATE BANKRUPTCY PREDICTION IN ROMANIA
Directory of Open Access Journals (Sweden)
Daniel BRÎNDESCU – OLARIU
2016-06-01
Full Text Available The current paper proposes a methodology for bankruptcy prediction applicable for Romanian companies. Low bankruptcy frequencies registered in the past have limited the importance of bankruptcy prediction in Romania. The changes in the economic environment brought by the economic crisis, as well as by the entrance in the European Union, make the availability of performing bankruptcy assessment tools more important than ever before. The proposed methodology is centred on a multivariate model, developed through discriminant analysis. Financial ratios are employed as explanatory variables within the model. The study has included 53,252 yearly financial statements from the period 2007 – 2010, with the state of the companies being monitored until the end of 2012. It thus employs the largest sample ever used in Romanian research in the field of bankruptcy prediction, not targeting high levels of accuracy over isolated samples, but reliability and ease of use over the entire population.
Finding Similarities in Ancient Ceramics by EDXRF and Multivariate Methods
International Nuclear Information System (INIS)
Civici, N.; Stamati, F.
1999-01-01
We have studied 39 samples of fragments from ceramic roof tiles with different stamps(Diamalas and Heraion), dated between 330 to 170 BC and found at the archaeological site of Dimales, some 30 km from the Adriatic coast. The data from these samples were compared with those obtained from 7 samples of similar objects and period with the stamp H eraion , found at the archaeological site of APOLLONIA. The samples were analyzed by energy-dispersive X -ray fluorescence(EDXRF), using of the x-ray lines of the elements to the intensity of the Compton peak. The results have been treated with diverse multivariate methods. The application of hierarchical cluster analysis and factor analysis permitted the identification of two main clusters. The first cluster is composed from the ''Heraion'' samples discovered in Apollonia, while the second comprises all the samples discovered in Dimale independent of their stamp. (authors)
Energy and economic growth in the USA: a multivariate approach
International Nuclear Information System (INIS)
Stern, D.I.
1993-01-01
This paper examines the casual relationship between Gross Domestic Product and energy use for the period 1947-90 in the United States of America. The relationship between energy use and economic growth has been examined by both biophysical and neoclassical economists. In particular, several studies have tested for the presence of a causal relationships (in the Granger sense) between energy use and economic growth. However, these tests do not allow a direct test of the relative explanatory powers of the neoclassical and biophysical models. A multivariate adaptation of the test-vector autoregression (VAR) does allow such a test. A VAR of GDP, energy use, capital stock and employment is estimated and Granger tests for causal relationships between the variables are carried out. Although there is no evidence that gross energy use Granger causes GDP, a measure of final energy use adjusted for changing fuel composition does Granger cause GDP. (author)
DEFF Research Database (Denmark)
Hansen, T.
The purpose of this Ph.D. thesis is twofold: The first purpose is to devise a new method for application of multivariable controllers in boiler control systems in which they act as optional process optimizing extensions to conventional control systems and in such a way that the safety measures...... mentioned, the concept is applicable to new as well as existing plants. The seccond purpose is to suggest specific methods for experimental modelling and multivariable controller design which are possible to use under the conceptual framework, implement them and test them in a boiler application....
Models and Inference for Multivariate Spatial Extremes
Vettori, Sabrina
2017-12-07
The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical
MIDAS: Regionally linear multivariate discriminative statistical mapping.
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
DEFF Research Database (Denmark)
Hansen, Peter Reinhard; Lunde, Asger; Voev, Valeri
We introduce a multivariate GARCH model that utilizes and models realized measures of volatility and covolatility. The realized measures extract information contained in high-frequency data that is particularly beneficial during periods with variation in volatility and covolatility. Applying the ...
Classification of adulterated honeys by multivariate analysis.
Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad
2017-06-01
In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.
Multivariate Functional Data Visualization and Outlier Detection
Dai, Wenlin; Genton, Marc G.
2017-01-01
This article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, which measures the centrality of functional data by simultaneously considering the level and the direction of their deviation from the central region. The MS-plot intuitively presents not only levels but also directions of magnitude outlyingness on the horizontal axis or plane, and demonstrates shape outlyingness on the vertical axis. A dividing curve or surface is provided to separate non-outlying data from the outliers. Both the simulated data and the practical examples confirm that the MS-plot is superior to existing tools for visualizing centrality and detecting outliers for functional data.
Multivariate Functional Data Visualization and Outlier Detection
Dai, Wenlin
2017-03-19
This article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, which measures the centrality of functional data by simultaneously considering the level and the direction of their deviation from the central region. The MS-plot intuitively presents not only levels but also directions of magnitude outlyingness on the horizontal axis or plane, and demonstrates shape outlyingness on the vertical axis. A dividing curve or surface is provided to separate non-outlying data from the outliers. Both the simulated data and the practical examples confirm that the MS-plot is superior to existing tools for visualizing centrality and detecting outliers for functional data.
Lectures in feedback design for multivariable systems
Isidori, Alberto
2017-01-01
This book focuses on methods that relate, in one form or another, to the “small-gain theorem”. It is aimed at readers who are interested in learning methods for the design of feedback laws for linear and nonlinear multivariable systems in the presence of model uncertainties. With worked examples throughout, it includes both introductory material and more advanced topics. Divided into two parts, the first covers relevant aspects of linear-systems theory, the second, nonlinear theory. In order to deepen readers’ understanding, simpler single-input–single-output systems generally precede treatment of more complex multi-input–multi-output (MIMO) systems and linear systems precede nonlinear systems. This approach is used throughout, including in the final chapters, which explain the latest advanced ideas governing the stabilization, regulation, and tracking of nonlinear MIMO systems. Two major design problems are considered, both in the presence of model uncertainties: asymptotic stabilization with a “...
Multivariate approach to matrimonial mobility in Catalonia.
Calafell, F; Hernández, M
1993-10-01
Matrimonial mobility in Catalonia was studied using 1986 census data. Comarca (a geographic division) of birth was used as the population unit, and a measure of affinity (a statistical distance) between comarques in spouse geographic origin was defined. This distance was analyzed with multivariate methods drawn from numerical taxonomy to detect any discontinuities in matrimonial mobility and gene flow between comarques. Results show a three-level pattern of gene flow in Catalonia: (1) a strong endogamy within comarques; (2) a 100-km matrimonial circle around every comarca; and (3) the capital, Barcelona, which attracts migrants from all over Catalonia. The regionalization in matrimonial mobility follows the geographically clear-cut groups of comarques almost exactly.
Nonparametric Bayes Modeling of Multivariate Categorical Data.
Dunson, David B; Xing, Chuanhua
2012-01-01
Modeling of multivariate unordered categorical (nominal) data is a challenging problem, particularly in high dimensions and cases in which one wishes to avoid strong assumptions about the dependence structure. Commonly used approaches rely on the incorporation of latent Gaussian random variables or parametric latent class models. The goal of this article is to develop a nonparametric Bayes approach, which defines a prior with full support on the space of distributions for multiple unordered categorical variables. This support condition ensures that we are not restricting the dependence structure a priori. We show this can be accomplished through a Dirichlet process mixture of product multinomial distributions, which is also a convenient form for posterior computation. Methods for nonparametric testing of violations of independence are proposed, and the methods are applied to model positional dependence within transcription factor binding motifs.
Some developments in multivariate image analysis
DEFF Research Database (Denmark)
Kucheryavskiy, Sergey
be up to several million. The main MIA tool for exploratory analysis is score density plot – all pixels are projected into principal component space and on the corresponding scores plots are colorized according to their density (how many pixels are crowded in the unit area of the plot). Looking...... for and analyzing patterns on these plots and the original image allow to do interactive analysis, to get some hidden information, build a supervised classification model, and much more. In the present work several alternative methods to original principal component analysis (PCA) for building the projection......Multivariate image analysis (MIA), one of the successful chemometric applications, now is used widely in different areas of science and industry. Introduced in late 80s it has became very popular with hyperspectral imaging, where MIA is one of the most efficient tools for exploratory analysis...
Multivariate analysis of data in sensory science
Naes, T; Risvik, E
1996-01-01
The state-of-the-art of multivariate analysis in sensory science is described in this volume. Both methods for aggregated and individual sensory profiles are discussed. Processes and results are presented in such a way that they can be understood not only by statisticians but also by experienced sensory panel leaders and users of sensory analysis. The techniques presented are focused on examples and interpretation rather than on the technical aspects, with an emphasis on new and important methods which are possibly not so well known to scientists in the field. Important features of the book are discussions on the relationship among the methods with a strong accent on the connection between problems and methods. All procedures presented are described in relation to sensory data and not as completely general statistical techniques. Sensory scientists, applied statisticians, chemometricians, those working in consumer science, food scientists and agronomers will find this book of value.
FACT. Multivariate extraction of muon ring images
Energy Technology Data Exchange (ETDEWEB)
Noethe, Maximilian; Temme, Fabian; Buss, Jens [Experimentelle Physik 5b, TU Dortmund, Dortmund (Germany); Collaboration: FACT-Collaboration
2016-07-01
In ground-based gamma-ray astronomy, muon ring images are an important event class for instrument calibration and monitoring of its properties. In this talk, a multivariate approach will be presented, that is well suited for real time extraction of muons from data streams of Imaging Atmospheric Cherenkov Telescopes (IACT). FACT, the First G-APD Cherenkov Telescope is located on the Canary Island of La Palma and is the first IACT to use Silicon Photomultipliers for detecting the Cherenkov photons of extensive air showers. In case of FACT, the extracted muon events are used to calculate the time resolution of the camera. In addition, the effect of the mirror alignment in May 2014 on properties of detected muons is investigated. Muon candidates are identified with a random forest classification algorithm. The performance of the classifier is evaluated for different sets of image parameters in order to compare the gain in performance with the computational costs of their calculation.
Validation of models with multivariate output
International Nuclear Information System (INIS)
Rebba, Ramesh; Mahadevan, Sankaran
2006-01-01
This paper develops metrics for validating computational models with experimental data, considering uncertainties in both. A computational model may generate multiple response quantities and the validation experiment might yield corresponding measured values. Alternatively, a single response quantity may be predicted and observed at different spatial and temporal points. Model validation in such cases involves comparison of multiple correlated quantities. Multiple univariate comparisons may give conflicting inferences. Therefore, aggregate validation metrics are developed in this paper. Both classical and Bayesian hypothesis testing are investigated for this purpose, using multivariate analysis. Since, commonly used statistical significance tests are based on normality assumptions, appropriate transformations are investigated in the case of non-normal data. The methodology is implemented to validate an empirical model for energy dissipation in lap joints under dynamic loading
Advanced event reweighting using multivariate analysis
International Nuclear Information System (INIS)
Martschei, D; Feindt, M; Honc, S; Wagner-Kuhr, J
2012-01-01
Multivariate analysis (MVA) methods, especially discrimination techniques such as neural networks, are key ingredients in modern data analysis and play an important role in high energy physics. They are usually trained on simulated Monte Carlo (MC) samples to discriminate so called 'signal' from 'background' events and are then applied to data to select real events of signal type. We here address procedures that improve this work flow. This will be the enhancement of data / MC agreement by reweighting MC samples on a per event basis. Then training MVAs on real data using the sPlot technique will be discussed. Finally we will address the construction of MVAs whose discriminator is independent of a certain control variable, i.e. cuts on this variable will not change the discriminator shape.
Multivariate Markov chain modeling for stock markets
Maskawa, Jun-ichi
2003-06-01
We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation. The time series of price changes are coded into the sequences of up and down spins according to their signs. We start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation. The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.
Derivatives of Multivariate Bernstein Operators and Smoothness with Jacobi Weights
Directory of Open Access Journals (Sweden)
Jianjun Wang
2012-01-01
Full Text Available Using the modulus of smoothness, directional derivatives of multivariate Bernstein operators with weights are characterized. The obtained results partly generalize the corresponding ones for multivariate Bernstein operators without weights.
Regularized multivariate regression models with skew-t error distributions
Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi
2014-01-01
We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both
Graphics for the multivariate two-sample problem
International Nuclear Information System (INIS)
Friedman, J.H.; Rafsky, L.C.
1981-01-01
Some graphical methods for comparing multivariate samples are presented. These methods are based on minimal spanning tree techniques developed for multivariate two-sample tests. The utility of these methods is illustrated through examples using both real and artificial data
Multivariate Receptor Models for Spatially Correlated Multipollutant Data
Jun, Mikyoung; Park, Eun Sug
2013-01-01
The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air
Marshall-Olkin multivariate semi-logistic distribution and minification ...
African Journals Online (AJOL)
Olkin multivariate logistic distribution (MO-ML) are introduced and studied. Various characterizations properties of Marshall-Olkin multivariate semi-logistic distribution are investigated and studied. First order autoregressive minification processes ...
Scale and shape mixtures of multivariate skew-normal distributions
Arellano-Valle, Reinaldo B.; Ferreira, Clé cio S.; Genton, Marc G.
2018-01-01
We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down
Energy Technology Data Exchange (ETDEWEB)
Junod, E [Commissariat a l' Energie Atomique Grenoble (France). Centre d' Etudes Nucleaires
1966-03-01
est consacree au probleme des corrections de pertes au comptage dues au temps mort des analyseurs multicanaux. On a etabli les formules de correction exactes lorsque le radionucleide de periode courte est pur ou seulement accompagne d'un radionucleide de periode longue. Par comparaison on a trace sous forme d'abaques les approximations fournies par le comptage dit 'en temps actif' et par un mode de comptage en temps reel associe a la mesure du temps mort global, ce second procede se revelant plus valable que le premier. Dans le cas d'un melange complexe, on donne le moyen de le ramener au probleme a deux constituants. Partie C: On presente les problemes que pose l'analyse qualitative et quantitative des courbes de decroissance d'un melange de sources radioactives dont au moins une de periode courte. Six procedes fondamentaux sont decrits mathematiquement pour lesquels sont proposes des elements de programmes fortran. Deux programmes supplementaires sont construits pour traiter globalement des problemes de dosages en analyse par activation: l'un base sur l'irradiation simultanee d'un echantillon et d'un ou plusieurs temoins; l'autre sur l'irradiation separee de l'echantillon et du temoin, un dosimetre (par activation ou exterieur) servant a normaliser les conditions de flux d'irradiation.
Multivariate Pareto Minification Processes | Umar | Journal of the ...
African Journals Online (AJOL)
Autoregressive (AR) and autoregressive moving average (ARMA) processes with multivariate exponential (ME) distribution are presented and discussed. The theory of positive dependence is used to show that in many cases, multivariate exponential autoregressive (MEAR) and multivariate autoregressive moving average ...
Multivariate semi-logistic distribution and processes | Umar | Journal ...
African Journals Online (AJOL)
Multivariate semi-logistic distribution is introduced and studied. Some characterizations properties of multivariate semi-logistic distribution are presented. First order autoregressive minification processes and its generalization to kth order autoregressive minification processes with multivariate semi-logistic distribution as ...
Multivariable biorthogonal continuous--discrete Wilson and Racah polynomials
International Nuclear Information System (INIS)
Tratnik, M.V.
1990-01-01
Several families of multivariable, biorthogonal, partly continuous and partly discrete, Wilson polynomials are presented. These yield limit cases that are purely continuous in some of the variables and purely discrete in the others, or purely discrete in all the variables. The latter are referred to as the multivariable biorthogonal Racah polynomials. Interesting further limit cases include the multivariable biorthogonal Hahn and dual Hahn polynomials
Energy Technology Data Exchange (ETDEWEB)
Vila Socias, L.; Prats Pico, N.; Buxeda Garrigos, J.
2016-05-01
Amphorae were the most popular transport vessels used to ship wine and other commodities in Antiquity. This paper reports the results of the analyses of 38 samples of several Roman amphorae types recovered at Calonge (Gerona, Spain). Chemical composition of individuals have been obtained by means of XRF. Equivalent firing temperatures have been estimated by XRD and further refiring experiments have been performed. The results suggest the existence of two different clay pastes among individuals analyzed, most of which were fired at the range 900-1000 degree centigrade. Moreover, it is argued that craftsmen did not follow highly standardized paste preparation process during the first activity period of the workshop. The results are discussed in terms of production technology and standardization. (Author)
Characterization of Lavandula spp. Honey Using Multivariate Techniques.
Estevinho, Leticia M; Chambó, Emerson Dechechi; Pereira, Ana Paula Rodrigues; Carvalho, Carlos Alfredo Lopes de; Toledo, Vagner de Alencar Arnaut de
2016-01-01
Traditionally, melissopalynological and physicochemical analyses have been the most used to determine the botanical origin of honey. However, when performed individually, these analyses may provide less unambiguous results, making it difficult to discriminate between mono and multifloral honeys. In this context, with the aim of better characterizing this beehive product, a selection of 112 Lavandula spp. monofloral honey samples from several regions were evaluated by association of multivariate statistical techniques with physicochemical, melissopalynological and phenolic compounds analysis. All honey samples fulfilled the quality standards recommended by international legislation, except regarding sucrose content and diastase activity. The content of sucrose and the percentage of Lavandula spp. pollen have a strong positive association. In fact, it was found that higher amounts of sucrose in honey are related with highest percentage of pollen of Lavandula spp.. The samples were very similar for most of the physicochemical parameters, except for proline, flavonoids and phenols (bioactive factors). Concerning the pollen spectrum, the variation of Lavandula spp. pollen percentage in honey had little contribution to the formation of samples groups. The formation of two groups regarding the physicochemical parameters suggests that the presence of other pollen types in small percentages influences the factor termed as "bioactive", which has been linked to diverse beneficial health effects.
Multivariate Birnbaum-Saunders Distributions: Modelling and Applications
Directory of Open Access Journals (Sweden)
Robert G. Aykroyd
2018-03-01
Full Text Available Since its origins and numerous applications in material science, the Birnbaum–Saunders family of distributions has now found widespread uses in some areas of the applied sciences such as agriculture, environment and medicine, as well as in quality control, among others. It is able to model varied data behaviour and hence provides a flexible alternative to the most usual distributions. The family includes Birnbaum–Saunders and log-Birnbaum–Saunders distributions in univariate and multivariate versions. There are now well-developed methods for estimation and diagnostics that allow in-depth analyses. This paper gives a detailed review of existing methods and of relevant literature, introducing properties and theoretical results in a systematic way. To emphasise the range of suitable applications, full analyses are included of examples based on regression and diagnostics in material science, spatial data modelling in agricultural engineering and control charts for environmental monitoring. However, potential future uses in new areas such as business, economics, finance and insurance are also discussed. This work is presented to provide a full tool-kit of novel statistical models and methods to encourage other researchers to implement them in these new areas. It is expected that the methods will have the same positive impact in the new areas as they have had elsewhere.
Directory of Open Access Journals (Sweden)
Jenicek Michal
2018-03-01
Full Text Available The knowledge of snowpack distribution at a catchment scale is important to predict the snowmelt runoff. The objective of this study is to select and quantify the most important factors governing the snowpack distribution, with special interest in the role of different canopy structure. We applied a simple distributed sampling design with measurement of snow depth and snow water equivalent (SWE at a catchment scale. We selected eleven predictors related to character of specific localities (such as elevation, slope orientation and leaf area index and to winter meteorological conditions (such as irradiance, sum of positive air temperature and sum of new snow depth. The forest canopy structure was described using parameters calculated from hemispherical photographs. A degree-day approach was used to calculate melt factors. Principal component analysis, cluster analysis and Spearman rank correlation were applied to reduce the number of predictors and to analyze measured data. The SWE in forest sites was by 40% lower than in open areas, but this value depended on the canopy structure. The snow ablation in large openings was on average almost two times faster compared to forest sites. The snow ablation in the forest was by 18% faster after forest defoliation (due to the bark beetle. The results from multivariate analyses showed that the leaf area index was a better predictor to explain the SWE distribution during accumulation period, while irradiance was better predictor during snowmelt period. Despite some uncertainty, parameters derived from hemispherical photographs may replace measured incoming solar radiation if this meteorological variable is not available.
Method for statistical data analysis of multivariate observations
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
Llewellyn, Clare H; van Jaarsveld, Cornelia H M; Plomin, Robert; Fisher, Abigail; Wardle, Jane
2012-03-01
The behavioral susceptibility model proposes that inherited differences in traits such as appetite confer differential risk of weight gain and contribute to the heritability of weight. Evidence that the FTO gene may influence weight partly through its effects on appetite supports this model, but testing the behavioral pathways for multiple genes with very small effects is not feasible. Twin analyses make it possible to get a broad-based estimate of the extent of shared genetic influence between appetite and weight. The objective was to use multivariate twin analyses to test the hypothesis that associations between appetite and weight are underpinned by shared genetic effects. Data were from Gemini, a population-based birth cohort of twins (n = 4804) born in 2007. Infant weights at 3 mo were taken from the records of health professionals. Appetite was assessed at 3 mo for the milk-feeding period by using the Baby Eating Behaviour Questionnaire (BEBQ), a parent-reported measure of appetite [enjoyment of food, food responsiveness, slowness in eating (SE), satiety responsiveness (SR), and appetite size (AS)]. Multivariate quantitative genetic modeling was used to test for shared genetic influences. Significant correlations were found between all BEBQ traits and weight. Significant shared genetic influence was identified for weight with SE, SR, and AS; genetic correlations were between 0.22 and 0.37. Shared genetic effects explained 41-45% of these phenotypic associations. Differences in weight in infancy may be due partly to genetically determined differences in appetitive traits that confer differential susceptibility to obesogenic environments.
Cross-covariance functions for multivariate geostatistics
Genton, Marc G.
2015-05-01
Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis challenges to scientists and statisticians. For many years, scientists developed models that aimed at capturing the spatial behavior for an individual process; only within the last few decades has it become commonplace to model multiple processes jointly. The key difficulty is in specifying the cross-covariance function, that is, the function responsible for the relationship between distinct variables. Indeed, these cross-covariance functions must be chosen to be consistent with marginal covariance functions in such a way that the second-order structure always yields a nonnegative definite covariance matrix. We review the main approaches to building cross-covariance models, including the linear model of coregionalization, convolution methods, the multivariate Matérn and nonstationary and space-time extensions of these among others. We additionally cover specialized constructions, including those designed for asymmetry, compact support and spherical domains, with a review of physics-constrained models. We illustrate select models on a bivariate regional climate model output example for temperature and pressure, along with a bivariate minimum and maximum temperature observational dataset; we compare models by likelihood value as well as via cross-validation co-kriging studies. The article closes with a discussion of unsolved problems. © Institute of Mathematical Statistics, 2015.
Multivariate Welch t-test on distances.
Alekseyenko, Alexander V
2016-12-01
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, however, suffers from loss of power and type I error inflation in the presence of heteroscedasticity and sample size imbalances. We develop a solution in the form of a distance-based Welch t-test, [Formula: see text], for two sample potentially unbalanced and heteroscedastic data. We demonstrate empirically the desirable type I error and power characteristics of the new test. We compare the performance of PERMANOVA and [Formula: see text] in reanalysis of two existing microbiome datasets, where the methodology has originated. The source code for methods and analysis of this article is available at https://github.com/alekseyenko/Tw2 Further guidance on application of these methods can be obtained from the author. alekseye@musc.edu. © The Author 2016. Published by Oxford University Press.
Multivariate sensitivity to voice during auditory categorization.
Lee, Yune Sang; Peelle, Jonathan E; Kraemer, David; Lloyd, Samuel; Granger, Richard
2015-09-01
Past neuroimaging studies have documented discrete regions of human temporal cortex that are more strongly activated by conspecific voice sounds than by nonvoice sounds. However, the mechanisms underlying this voice sensitivity remain unclear. In the present functional MRI study, we took a novel approach to examining voice sensitivity, in which we applied a signal detection paradigm to the assessment of multivariate pattern classification among several living and nonliving categories of auditory stimuli. Within this framework, voice sensitivity can be interpreted as a distinct neural representation of brain activity that correctly distinguishes human vocalizations from other auditory object categories. Across a series of auditory categorization tests, we found that bilateral superior and middle temporal cortex consistently exhibited robust sensitivity to human vocal sounds. Although the strongest categorization was in distinguishing human voice from other categories, subsets of these regions were also able to distinguish reliably between nonhuman categories, suggesting a general role in auditory object categorization. Our findings complement the current evidence of cortical sensitivity to human vocal sounds by revealing that the greatest sensitivity during categorization tasks is devoted to distinguishing voice from nonvoice categories within human temporal cortex. Copyright © 2015 the American Physiological Society.
Multivariate volume visualization through dynamic projections
Energy Technology Data Exchange (ETDEWEB)
Liu, Shusen [Univ. of Utah, Salt Lake City, UT (United States); Wang, Bei [Univ. of Utah, Salt Lake City, UT (United States); Thiagarajan, Jayaraman J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Bremer, Peer -Timo [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States)
2014-11-01
We propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. As a result, using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space.
Scattering amplitudes from multivariate polynomial division
Energy Technology Data Exchange (ETDEWEB)
Mastrolia, Pierpaolo, E-mail: pierpaolo.mastrolia@cern.ch [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany); Dipartimento di Fisica e Astronomia, Universita di Padova, Padova (Italy); INFN Sezione di Padova, via Marzolo 8, 35131 Padova (Italy); Mirabella, Edoardo, E-mail: mirabell@mppmu.mpg.de [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany); Ossola, Giovanni, E-mail: GOssola@citytech.cuny.edu [New York City College of Technology, City University of New York, 300 Jay Street, Brooklyn, NY 11201 (United States); Graduate School and University Center, City University of New York, 365 Fifth Avenue, New York, NY 10016 (United States); Peraro, Tiziano, E-mail: peraro@mppmu.mpg.de [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany)
2012-11-15
We show that the evaluation of scattering amplitudes can be formulated as a problem of multivariate polynomial division, with the components of the integration-momenta as indeterminates. We present a recurrence relation which, independently of the number of loops, leads to the multi-particle pole decomposition of the integrands of the scattering amplitudes. The recursive algorithm is based on the weak Nullstellensatz theorem and on the division modulo the Groebner basis associated to all possible multi-particle cuts. We apply it to dimensionally regulated one-loop amplitudes, recovering the well-known integrand-decomposition formula. Finally, we focus on the maximum-cut, defined as a system of on-shell conditions constraining the components of all the integration-momenta. By means of the Finiteness Theorem and of the Shape Lemma, we prove that the residue at the maximum-cut is parametrized by a number of coefficients equal to the number of solutions of the cut itself.
Cross-covariance functions for multivariate geostatistics
Genton, Marc G.; Kleiber, William
2015-01-01
Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis challenges to scientists and statisticians. For many years, scientists developed models that aimed at capturing the spatial behavior for an individual process; only within the last few decades has it become commonplace to model multiple processes jointly. The key difficulty is in specifying the cross-covariance function, that is, the function responsible for the relationship between distinct variables. Indeed, these cross-covariance functions must be chosen to be consistent with marginal covariance functions in such a way that the second-order structure always yields a nonnegative definite covariance matrix. We review the main approaches to building cross-covariance models, including the linear model of coregionalization, convolution methods, the multivariate Matérn and nonstationary and space-time extensions of these among others. We additionally cover specialized constructions, including those designed for asymmetry, compact support and spherical domains, with a review of physics-constrained models. We illustrate select models on a bivariate regional climate model output example for temperature and pressure, along with a bivariate minimum and maximum temperature observational dataset; we compare models by likelihood value as well as via cross-validation co-kriging studies. The article closes with a discussion of unsolved problems. © Institute of Mathematical Statistics, 2015.
Multivariate Heteroscedasticity Models for Functional Brain Connectivity
Directory of Open Access Journals (Sweden)
Christof Seiler
2017-12-01
Full Text Available Functional brain connectivity is the co-occurrence of brain activity in different areas during resting and while doing tasks. The data of interest are multivariate timeseries measured simultaneously across brain parcels using resting-state fMRI (rfMRI. We analyze functional connectivity using two heteroscedasticity models. Our first model is low-dimensional and scales linearly in the number of brain parcels. Our second model scales quadratically. We apply both models to data from the Human Connectome Project (HCP comparing connectivity between short and conventional sleepers. We find stronger functional connectivity in short than conventional sleepers in brain areas consistent with previous findings. This might be due to subjects falling asleep in the scanner. Consequently, we recommend the inclusion of average sleep duration as a covariate to remove unwanted variation in rfMRI studies. A power analysis using the HCP data shows that a sample size of 40 detects 50% of the connectivity at a false discovery rate of 20%. We provide implementations using R and the probabilistic programming language Stan.
Hierarchical multivariate covariance analysis of metabolic connectivity.
Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J
2014-12-01
Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).
Multivariate analysis of quantitative traits can effectively classify rapeseed germplasm
Directory of Open Access Journals (Sweden)
Jankulovska Mirjana
2014-01-01
Full Text Available In this study, the use of different multivariate approaches to classify rapeseed genotypes based on quantitative traits has been presented. Tree regression analysis, PCA analysis and two-way cluster analysis were applied in order todescribe and understand the extent of genetic variability in spring rapeseed genotype by trait data. The traits which highly influenced seed and oil yield in rapeseed were successfully identified by the tree regression analysis. Principal predictor for both response variables was number of pods per plant (NP. NP and 1000 seed weight could help in the selection of high yielding genotypes. High values for both traits and oil content could lead to high oil yielding genotypes. These traits may serve as indirect selection criteria and can lead to improvement of seed and oil yield in rapeseed. Quantitative traits that explained most of the variability in the studied germplasm were classified using principal component analysis. In this data set, five PCs were identified, out of which the first three PCs explained 63% of the total variance. It helped in facilitating the choice of variables based on which the genotypes’ clustering could be performed. The two-way cluster analysissimultaneously clustered genotypes and quantitative traits. The final number of clusters was determined using bootstrapping technique. This approach provided clear overview on the variability of the analyzed genotypes. The genotypes that have similar performance regarding the traits included in this study can be easily detected on the heatmap. Genotypes grouped in the clusters 1 and 8 had high values for seed and oil yield, and relatively short vegetative growth duration period and those in cluster 9, combined moderate to low values for vegetative growth duration and moderate to high seed and oil yield. These genotypes should be further exploited and implemented in the rapeseed breeding program. The combined application of these multivariate methods
Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology.
Hervé, Maxime R; Nicolè, Florence; Lê Cao, Kim-Anh
2018-03-01
Chemical ecology has strong links with metabolomics, the large-scale study of all metabolites detectable in a biological sample. Consequently, chemical ecologists are often challenged by the statistical analyses of such large datasets. This holds especially true when the purpose is to integrate multiple datasets to obtain a holistic view and a better understanding of a biological system under study. The present article provides a comprehensive resource to analyze such complex datasets using multivariate methods. It starts from the necessary pre-treatment of data including data transformations and distance calculations, to the application of both gold standard and novel multivariate methods for the integration of different omics data. We illustrate the process of analysis along with detailed results interpretations for six issues representative of the different types of biological questions encountered by chemical ecologists. We provide the necessary knowledge and tools with reproducible R codes and chemical-ecological datasets to practice and teach multivariate methods.
Li, Yanming; Nan, Bin; Zhu, Ji
2015-06-01
We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functional groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. © 2015, The International Biometric Society.
van der Ham, Joris L
2016-05-19
Forensic entomologists can use carrion communities' ecological succession data to estimate the postmortem interval (PMI). Permutation tests of hierarchical cluster analyses of these data provide a conceptual method to estimate part of the PMI, the post-colonization interval (post-CI). This multivariate approach produces a baseline of statistically distinct clusters that reflect changes in the carrion community composition during the decomposition process. Carrion community samples of unknown post-CIs are compared with these baseline clusters to estimate the post-CI. In this short communication, I use data from previously published studies to demonstrate the conceptual feasibility of this multivariate approach. Analyses of these data produce series of significantly distinct clusters, which represent carrion communities during 1- to 20-day periods of the decomposition process. For 33 carrion community samples, collected over an 11-day period, this approach correctly estimated the post-CI within an average range of 3.1 days. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Özdemir, Durmuş
2006-01-01
Determination of wheat flour quality parameters, such as protein, moisture, dry mass by wet chemistry analyses takes long time. Near infrared spectroscopy (NIR) coupled with multivariate calibration offers a fast and nondestructive alternative to obtain reliable results. However, due to the complexity of the spectra obtained from NIR, some wavelength selection is generally required to improve the predictive ability of multivariate calibration methods. In this study, two different wheat data s...
Huang, Jun; Kaul, Goldi; Cai, Chunsheng; Chatlapalli, Ramarao; Hernandez-Abad, Pedro; Ghosh, Krishnendu; Nagi, Arwinder
2009-12-01
To facilitate an in-depth process understanding, and offer opportunities for developing control strategies to ensure product quality, a combination of experimental design, optimization and multivariate techniques was integrated into the process development of a drug product. A process DOE was used to evaluate effects of the design factors on manufacturability and final product CQAs, and establish design space to ensure desired CQAs. Two types of analyses were performed to extract maximal information, DOE effect & response surface analysis and multivariate analysis (PCA and PLS). The DOE effect analysis was used to evaluate the interactions and effects of three design factors (water amount, wet massing time and lubrication time), on response variables (blend flow, compressibility and tablet dissolution). The design space was established by the combined use of DOE, optimization and multivariate analysis to ensure desired CQAs. Multivariate analysis of all variables from the DOE batches was conducted to study relationships between the variables and to evaluate the impact of material attributes/process parameters on manufacturability and final product CQAs. The integrated multivariate approach exemplifies application of QbD principles and tools to drug product and process development.
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.
Cichy, Radoslaw Martin; Pantazis, Dimitrios
2017-09-01
Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.
A simple ergonomic measure reduces fluoroscopy time during ERCP: A multivariate analysis.
Jowhari, Fahd; Hopman, Wilma M; Hookey, Lawrence
2017-03-01
Background and study aims Endoscopic retrograde cholangiopancreatgraphy (ERCP) carries a radiation risk to patients undergoing the procedure and the team performing it. Fluoroscopy time (FT) has been shown to have a linear relationship with radiation exposure during ERCP. Recent modifications to our ERCP suite design were felt to impact fluoroscopy time and ergonomics. This multivariate analysis was therefore undertaken to investigate these effects, and to identify and validate various clinical, procedural and ergonomic factors influencing the total fluoroscopy time during ERCP. This would better assist clinicians with predicting prolonged fluoroscopic durations and to undertake relevant precautions accordingly. Patients and methods A retrospective analysis of 299 ERCPs performed by 4 endoscopists over an 18-month period, at a single tertiary care center was conducted. All inpatients/outpatients (121 males, 178 females) undergoing ERCP for any clinical indication from January 2012 to June 2013 in the chosen ERCP suite were included in the study. Various predetermined clinical, procedural and ergonomic factors were obtained via chart review. Univariate analyses identified factors to be included in the multivariate regression model with FT as the dependent variable. Results Bringing the endoscopy and fluoroscopy screens next to each other was associated with a significantly lesser FT than when the screens were separated further (-1.4 min, P = 0.026). Other significant factors associated with a prolonged FT included having a prior ERCP (+ 1.4 min, P = 0.031), and more difficult procedures (+ 4.2 min for each level of difficulty, P < 0.001). ERCPs performed by high-volume endoscopists used lesser FT vs. low-volume endoscopists (-1.82, P = 0.015). Conclusions Our study has identified and validated various factors that affect the total fluoroscopy time during ERCP. This is the first study to show that decreasing the distance
Determination of sulfamethoxazole and trimethoprim mixtures by multivariate electronic spectroscopy
Cordeiro, Gilcélia A.; Peralta-Zamora, Patricio; Nagata, Noemi; Pontarollo, Roberto
2008-01-01
In this work a multivariate spectroscopic methodology is proposed for quantitative determination of sulfamethoxazole and trimethoprim in pharmaceutical associations. The multivariate model was developed by partial least-squares regression, using twenty synthetic mixtures and the spectral region between 190 and 350 nm. In the validation stage, which involved the analysis of five synthetic mixtures, prediction errors lower that 3% were observed. The predictive capacity of the multivariate model...
Evolution of periodicity in periodical cicadas.
Ito, Hiromu; Kakishima, Satoshi; Uehara, Takashi; Morita, Satoru; Koyama, Takuya; Sota, Teiji; Cooley, John R; Yoshimura, Jin
2015-09-14
Periodical cicadas (Magicicada spp.) in the USA are famous for their unique prime-numbered life cycles of 13 and 17 years and their nearly perfectly synchronized mass emergences. Because almost all known species of cicada are non-periodical, periodicity is assumed to be a derived state. A leading hypothesis for the evolution of periodicity in Magicicada implicates the decline in average temperature during glacial periods. During the evolution of periodicity, the determinant of maturation in ancestral cicadas is hypothesized to have switched from size dependence to time (period) dependence. The selection for the prime-numbered cycles should have taken place only after the fixation of periodicity. Here, we build an individual-based model of cicadas under conditions of climatic cooling to explore the fixation of periodicity. In our model, under cold environments, extremely long juvenile stages lead to extremely low adult densities, limiting mating opportunities and favouring the evolution of synchronized emergence. Our results indicate that these changes, which were triggered by glacial cooling, could have led to the fixation of periodicity in the non-periodical ancestors.
Ormoli, Leonardo; Costa, Corrado; Negri, Stefano; Perenzin, Maurizio; Vaccino, Patrizia
2015-02-25
A collection of 157 Triticum aestivum accessions, representative of wheat breeding in Italy during the 20(th) century, was assembled to describe the evolutionary trends of cultivated varieties throughout this period. The lines were cultivated in Italy, in two locations, over two growing seasons, and evaluated for several agronomical, morphological and qualitative traits. Analyses were conducted using the most common univariate approach on individual plant traits coupled with a correspondance multivariate approach. ANOVA showed a clear trend from old to new varieties, leading towards earliness, plant height reduction and denser spikes with smaller seeds. The average protein content gradually decreased over time; however this trend did not affect bread-making quality, because it was counterbalanced by a gradual increase of SDS sedimentation volume, achieved by the incorporation of favourable alleles into recent cultivars. Correspondence analysis allowed an overall view of the breeding activity. A clear-cut separation was observed between ancient lines and all the others, matched with a two-step gradient, the first, corresponding roughly to the period 1920-1940, which can be ascribed mostly to genetics, the second, from the 40s onward, which can be ascribed also to the farming practice innovations, such as improvement of mechanical devices and optimised use of fertilizers.
Multivariate Bonferroni-type inequalities theory and applications
Chen, John
2014-01-01
Multivariate Bonferroni-Type Inequalities: Theory and Applications presents a systematic account of research discoveries on multivariate Bonferroni-type inequalities published in the past decade. The emergence of new bounding approaches pushes the conventional definitions of optimal inequalities and demands new insights into linear and Fréchet optimality. The book explores these advances in bounding techniques with corresponding innovative applications. It presents the method of linear programming for multivariate bounds, multivariate hybrid bounds, sub-Markovian bounds, and bounds using Hamil
Multivariate methods in nuclear waste remediation: Needs and applications
International Nuclear Information System (INIS)
Pulsipher, B.A.
1992-05-01
The United States Department of Energy (DOE) has developed a strategy for nuclear waste remediation and environmental restoration at several major sites across the country. Nuclear and hazardous wastes are found in underground storage tanks, containment drums, soils, and facilities. Due to the many possible contaminants and complexities of sampling and analysis, multivariate methods are directly applicable. However, effective application of multivariate methods will require greater ability to communicate methods and results to a non-statistician community. Moreover, more flexible multivariate methods may be required to accommodate inherent sampling and analysis limitations. This paper outlines multivariate applications in the context of select DOE environmental restoration activities and identifies several perceived needs
Multivariate statistics high-dimensional and large-sample approximations
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
Oil price and financial markets: Multivariate dynamic frequency analysis
International Nuclear Information System (INIS)
Creti, Anna; Ftiti, Zied; Guesmi, Khaled
2014-01-01
The aim of this paper is to study the degree of interdependence between oil price and stock market index into two groups of countries: oil-importers and oil-exporters. To this end, we propose a new empirical methodology allowing a time-varying dynamic correlation measure between the stock market index and the oil price series. We use the frequency approach proposed by Priestley and Tong (1973), that is the evolutionary co-spectral analysis. This method allows us to distinguish between short-run and medium-run dependence. In order to complete our study by analysing long-run dependence, we use the cointegration procedure developed by Engle and Granger (1987). We find that interdependence between the oil price and the stock market is stronger in exporters' markets than in the importers' ones. - Highlights: • A new time-varying measure for the stock markets and oil price relationship in different horizons. • We propose a new empirical methodology: multivariate frequency approach. • We propose a comparison between oil importing and exporting countries. • We show that oil is not always countercyclical with respect to stock markets. • When high oil prices originate from supply shocks, oil is countercyclical with stock markets
Adjustment of geochemical background by robust multivariate statistics
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.
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)
A multivariate rank test for comparing mass size distributions
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.
Elemental content of Vietnamese rice. Part 2. Multivariate data analysis.
Kokot, S; Phuong, T D
1999-04-01
Rice samples were obtained from the Red River region and some other parts of Vietnam as well as from Yanco, Australia. These samples were analysed for 14 elements (P, K, Mg, Ca, Mn, Zn, Fe, Cu, Al, Na, Ni, As, Mo and Cd) by ICP-AES, ICP-MS and FAAS as described in Part 1. This data matrix was then submitted to multivariate data analysis by principal component analysis to investigate the influences of environmental and crop cultivation variables on the elemental content of rice. Results revealed that geographical location, grain variety, seasons and soil conditions are the most likely significant factors causing changes in the elemental content between the rice samples. To assess rice quality according to its elemental content and physio-biological properties, a multicriteria decision making method (PROMETHEE) was applied. With the Vietnamese rice, the sticky rice appeared to contain somewhat higher levels of nutritionally significant elements such as P, K and Mg than the non-sticky rice. Also, rice samples grown during the wet season have better levels of nutritionally significant mineral elements than those of the dry season, but in general, the wet season seemed to provide better overall elemental and physio-biological rice quality.
Multivariable Techniques for High-Speed Research Flight Control Systems
Newman, Brett A.
1999-01-01
This report describes the activities and findings conducted under contract with NASA Langley Research Center. Subject matter is the investigation of suitable multivariable flight control design methodologies and solutions for large, flexible high-speed vehicles. Specifically, methodologies are to address the inner control loops used for stabilization and augmentation of a highly coupled airframe system possibly involving rigid-body motion, structural vibrations, unsteady aerodynamics, and actuator dynamics. Design and analysis techniques considered in this body of work are both conventional-based and contemporary-based, and the vehicle of interest is the High-Speed Civil Transport (HSCT). Major findings include: (1) control architectures based on aft tail only are not well suited for highly flexible, high-speed vehicles, (2) theoretical underpinnings of the Wykes structural mode control logic is based on several assumptions concerning vehicle dynamic characteristics, and if not satisfied, the control logic can break down leading to mode destabilization, (3) two-loop control architectures that utilize small forward vanes with the aft tail provide highly attractive and feasible solutions to the longitudinal axis control challenges, and (4) closed-loop simulation sizing analyses indicate the baseline vane model utilized in this report is most likely oversized for normal loading conditions.
Multivariate NIR studies of seed-water interaction in Scots Pine Seeds (Pinus sylvestris L.)
Lestander, Torbjörn
2003-01-01
This thesis describes seed-water interaction using near infrared (NIR) spectroscopy, multivariate regression models and Scots pine seeds. The presented research covers classification of seed viability, prediction of seed moisture content, selection of NIR wavelengths and interpretation of seed-water interaction modelled and analysed by principal component analysis, ordinary least squares (OLS), partial least squares (PLS), bi-orthogonal least squares (BPLS) and genetic algorithms. The potenti...
Hidden Markov latent variable models with multivariate longitudinal data.
Song, Xinyuan; Xia, Yemao; Zhu, Hongtu
2017-03-01
Cocaine addiction is chronic and persistent, and has become a major social and health problem in many countries. Existing studies have shown that cocaine addicts often undergo episodic periods of addiction to, moderate dependence on, or swearing off cocaine. Given its reversible feature, cocaine use can be formulated as a stochastic process that transits from one state to another, while the impacts of various factors, such as treatment received and individuals' psychological problems on cocaine use, may vary across states. This article develops a hidden Markov latent variable model to study multivariate longitudinal data concerning cocaine use from a California Civil Addict Program. The proposed model generalizes conventional latent variable models to allow bidirectional transition between cocaine-addiction states and conventional hidden Markov models to allow latent variables and their dynamic interrelationship. We develop a maximum-likelihood approach, along with a Monte Carlo expectation conditional maximization (MCECM) algorithm, to conduct parameter estimation. The asymptotic properties of the parameter estimates and statistics for testing the heterogeneity of model parameters are investigated. The finite sample performance of the proposed methodology is demonstrated by simulation studies. The application to cocaine use study provides insights into the prevention of cocaine use. © 2016, The International Biometric Society.
Directory of Open Access Journals (Sweden)
2016-12-01
Full Text Available This paper is on data analysis strategy in a complex, multidimensional, and dynamic domain. The focus is on the use of data mining techniques to explore the importance of multivariate structures; using climate variables which influences climate change. Techniques involved in data mining exercise vary according to the data structures. The multivariate analysis strategy considered here involved choosing an appropriate tool to analyze a process. Factor analysis is introduced into data mining technique in order to reveal the influencing impacts of factors involved as well as solving for multicolinearity effect among the variables. The temporal nature and multidimensionality of the target variables is revealed in the model using multidimensional regression estimates. The strategy of integrating the method of several statistical techniques, using climate variables in Nigeria was employed. R2 of 0.518 was obtained from the ordinary least square regression analysis carried out and the test was not significant at 5% level of significance. However, factor analysis regression strategy gave a good fit with R2 of 0.811 and the test was significant at 5% level of significance. Based on this study, model building should go beyond the usual confirmatory data analysis (CDA, rather it should be complemented with exploratory data analysis (EDA in order to achieve a desired result.
Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.
Directory of Open Access Journals (Sweden)
Binod Neupane
Full Text Available In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when
Multivariate analysis of prognostic factors in male breast cancer in Serbia.
Sipetic-Grujicic, Sandra Branko; Murtezani, Zafir Hajdar; Neskovic-Konstatinovic, Zora Borivoje; Marinkovic, Jelena Milutin; Kovcin, Vladimir Nikola; Andric, Zoran Gojko; Kostic, Sanja Vladeta; Ratkov, Isidora Stojan; Maksimovic, Jadranka Milutin
2014-01-01
The aim of this study was to analyze the demographic and clinical characteristics of male breast cancer patients in Serbia, and furthermore to determine overall survival and predictive factors for prognosis. In the period of 1996-2006 histopathological diagnosis of breast cancer was made in 84 males at the Institute for Oncology and Radiology of Serbia. For statistical analyses the Kaplan-Meier method, long-rank test and Cox proportional hazards regression model were used. The mean age at diagnosis with breast cancer was 64.3±10.5 years with a range from 35-84 years. Nearly 80% of the tumors showed ductal histology. About 44% had early tumor stages (I and II) whereas 46.4% and 9.5% of the male exhibited stages III and IV, respectively. Only 7.1% of male patients were grade one. One-fifth of all patients had tumors measuring ≤2 cm, and 14.3% larger than 5 cm. Lymph node metastasis was recorded in 40.4% patients and 47% relapse. Estrogen and progesterone receptor expression was positive in 66.7% and 58.3%, respectively. Among 14.3% of individuals tumor was HER2 positive. About two-thirds of all male patients had radical mastectomy (66.7%). Adjuvant hormonal (tamoxifene), systematic chemotherapy (CMF or FAC) and adjuvant radiotherapy were given to 59.5%, 35.7% and 29.8% patients respectively. Overall survival rates at five and ten years for male breast cancer were 55.0% and 43.9%, respectively. According to the multivariate Cox regression predictive model, a lower initial disease stage, a lower tumor grade, application of adjuvant hormone therapy and no relapse occurrence were significant independent predictors for good overall survival. Results of the treatment would be better if disease is discovered earlier and therefore health education and screening are an imperative in solving this problem.
Khobai, Hlalefang
2018-01-01
This paper applied the ARDL bounds test approach and the VECM test technique to examine the long run relationship and direction of causality between renewable energy consumption and economic growth in Argentina. Quarterly time series data was employed in this study covering a period between 1990 and 2014. Trade openness, capital and employment were included in the study to form a multivariate framework. The results established that there is a long run relationship between the variables. The V...
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
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.
Simulations of full multivariate Tweedie with flexible dependence structure
DEFF Research Database (Denmark)
Cuenin, Johann; Jørgensen, Bent; Kokonendji, Célestin C.
2016-01-01
The paper introduces a variables-in-common method for constructing and simulating multivariate Tweedie distribution, based on linear combinations of independent univariate Tweedie variables. The method is facilitated by the convolution and scaling properties of the Tweedie distributions, using....... The method allows simulation of multivariate distributions from many known, including the Gaussian, Poisson, non-central gamma, gamma and inverse Gaussian distributions....
Multivariate missing data in hydrology - Review and applications
Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.
2017-12-01
Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.
I - Multivariate Classification and Machine Learning in HEP
CERN. Geneva
2016-01-01
Traditional multivariate methods for classification (Stochastic Gradient Boosted Decision Trees and Multi-Layer Perceptrons) are explained in theory and practise using examples from HEP. General aspects of multivariate classification are discussed, in particular different regularisation techniques. Afterwards, data-driven techniques are introduced and compared to MC-based methods.
The analysis of multivariate group differences using common principal components
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
Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models
Price, Larry R.
2012-01-01
The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…
Multivariate Analysis and Machine Learning in Cerebral Palsy Research
Directory of Open Access Journals (Sweden)
Jing Zhang
2017-12-01
Full Text Available Cerebral palsy (CP, a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.
Multivariate Analysis and Machine Learning in Cerebral Palsy Research.
Zhang, Jing
2017-01-01
Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.
A Range-Based Multivariate Model for Exchange Rate Volatility
B. Tims (Ben); R.J. Mahieu (Ronald)
2003-01-01
textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are
Multivariate statistical methods and data mining in particle physics (4/4)
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.
Multivariate statistical methods and data mining in particle physics (2/4)
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.
Multivariate statistical methods and data mining in particle physics (1/4)
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.
A novel multi-variant epitope ensemble vaccine against avian leukosis virus subgroup J.
Wang, Xiaoyu; Zhou, Defang; Wang, Guihua; Huang, Libo; Zheng, Qiankun; Li, Chengui; Cheng, Ziqiang
2017-12-04
The hypervariable antigenicity and immunosuppressive features of avian leukosis virus subgroup J (ALV-J) has led to great challenges to develop effective vaccines. Epitope vaccine will be a perspective trend. Previously, we identified a variant antigenic neutralizing epitope in hypervariable region 1 (hr1) of ALV-J, N-LRDFIA/E/TKWKS/GDDL/HLIRPYVNQS-C. BLAST analysis showed that the mutation of A, E, T and H in this epitope cover 79% of all ALV-J strains. Base on this data, we designed a multi-variant epitope ensemble vaccine comprising the four mutation variants linked with glycine and serine. The recombinant multi-variant epitope gene was expressed in Escherichia coli BL21. The expressed protein of the variant multi-variant epitope gene can react with positive sera and monoclonal antibodies of ALV-J, while cannot react with ALV-J negative sera. The multi-variant epitope vaccine that conjugated Freund's adjuvant complete/incomplete showed high immunogenicity that reached the titer of 1:64,000 at 42 days post immunization and maintained the immune period for at least 126 days in SPF chickens. Further, we demonstrated that the antibody induced by the variant multi-variant ensemble epitope vaccine recognized and neutralized different ALV-J strains (NX0101, TA1, WS1, BZ1224 and BZ4). Protection experiment that was evaluated by clinical symptom, viral shedding, weight gain, gross and histopathology showed 100% chickens that inoculated the multi-epitope vaccine were well protected against ALV-J challenge. The result shows a promising multi-variant epitope ensemble vaccine against hypervariable viruses in animals. Copyright © 2017 Elsevier Ltd. All rights reserved.
Fractional and multivariable calculus model building and optimization problems
Mathai, A M
2017-01-01
This textbook presents a rigorous approach to multivariable calculus in the context of model building and optimization problems. This comprehensive overview is based on lectures given at five SERC Schools from 2008 to 2012 and covers a broad range of topics that will enable readers to understand and create deterministic and nondeterministic models. Researchers, advanced undergraduate, and graduate students in mathematics, statistics, physics, engineering, and biological sciences will find this book to be a valuable resource for finding appropriate models to describe real-life situations. The first chapter begins with an introduction to fractional calculus moving on to discuss fractional integrals, fractional derivatives, fractional differential equations and their solutions. Multivariable calculus is covered in the second chapter and introduces the fundamentals of multivariable calculus (multivariable functions, limits and continuity, differentiability, directional derivatives and expansions of multivariable ...
Yoon, Richard S; Gage, Mark J; Galos, David K; Donegan, Derek J; Liporace, Frank A
2017-06-01
Intramedullary nailing (IMN) has become the standard of care for the treatment of most femoral shaft fractures. Different IMN options include trochanteric and piriformis entry as well as retrograde nails, which may result in varying degrees of femoral rotation. The objective of this study was to analyze postoperative femoral version between three types of nails and to delineate any significant differences in femoral version (DFV) and revision rates. Over a 10-year period, 417 patients underwent IMN of a diaphyseal femur fracture (AO/OTA 32A-C). Of these patients, 316 met inclusion criteria and obtained postoperative computed tomography (CT) scanograms to calculate femoral version and were thus included in the study. In this study, our main outcome measure was the difference in femoral version (DFV) between the uninjured limb and the injured limb. The effect of the following variables on DFV and revision rates were determined via univariate, multivariate, and ordinal regression analyses: gender, age, BMI, ethnicity, mechanism of injury, operative side, open fracture, and table type/position. Statistical significance was set at pregression analysis revealed that a lower BMI was significantly associated with a lower DFV (p=0.006). Controlling for possible covariables, multivariate analysis yielded a significantly lower DFV for trochanteric entry nails than piriformis or retrograde nails (7.9±6.10 vs. 9.5±7.4 vs. 9.4±7.8°, pregression analysis. However, this is not to state that the other nail types exhibited abnormal DFV. Translation to the clinical impact of a few degrees of DFV is also unknown. Future studies to more in-depth study the intricacies of femoral version may lead to improved technology in addition to potentially improved clinical outcomes. Copyright © 2017 Elsevier Ltd. All rights reserved.
Davatzikos, Christos
2016-10-01
The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. Copyright © 2016. Published by Elsevier B.V.
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
The Multivariate Largest Lyapunov Exponent as an Age-Related Metric of Quiet Standing Balance
Directory of Open Access Journals (Sweden)
Kun Liu
2015-01-01
Full Text Available The largest Lyapunov exponent has been researched as a metric of the balance ability during human quiet standing. However, the sensitivity and accuracy of this measurement method are not good enough for clinical use. The present research proposes a metric of the human body’s standing balance ability based on the multivariate largest Lyapunov exponent which can quantify the human standing balance. The dynamic multivariate time series of ankle, knee, and hip were measured by multiple electrical goniometers. Thirty-six normal people of different ages participated in the test. With acquired data, the multivariate largest Lyapunov exponent was calculated. Finally, the results of the proposed approach were analysed and compared with the traditional method, for which the largest Lyapunov exponent and power spectral density from the centre of pressure were also calculated. The following conclusions can be obtained. The multivariate largest Lyapunov exponent has a higher degree of differentiation in differentiating balance in eyes-closed conditions. The MLLE value reflects the overall coordination between multisegment movements. Individuals of different ages can be distinguished by their MLLE values. The standing stability of human is reduced with the increment of age.
A Three-period Samuelson-Diamond Growth Model
DEFF Research Database (Denmark)
Blomgren-Hansen, Niels
2005-01-01
Samuelson (1958) analyses a three-period model, whereas Diamod (1965) considers a two-period model. This difference poses the question whether the insights derived by analysing the simple two-period model carry over in the more complicated three-period case. They do. The Samuelson model (no produ...
Multivariate Local Polynomial Regression with Application to Shenzhen Component Index
Directory of Open Access Journals (Sweden)
Liyun Su
2011-01-01
Full Text Available This study attempts to characterize and predict stock index series in Shenzhen stock market using the concepts of multivariate local polynomial regression. Based on nonlinearity and chaos of the stock index time series, multivariate local polynomial prediction methods and univariate local polynomial prediction method, all of which use the concept of phase space reconstruction according to Takens' Theorem, are considered. To fit the stock index series, the single series changes into bivariate series. To evaluate the results, the multivariate predictor for bivariate time series based on multivariate local polynomial model is compared with univariate predictor with the same Shenzhen stock index data. The numerical results obtained by Shenzhen component index show that the prediction mean squared error of the multivariate predictor is much smaller than the univariate one and is much better than the existed three methods. Even if the last half of the training data are used in the multivariate predictor, the prediction mean squared error is smaller than the univariate predictor. Multivariate local polynomial prediction model for nonsingle time series is a useful tool for stock market price prediction.
Lifetime risks for aneurysmal subarachnoid haemorrhage: multivariable risk stratification.
Vlak, Monique H M; Rinkel, Gabriel J E; Greebe, Paut; Greving, Jacoba P; Algra, Ale
2013-06-01
The overall incidence of aneurysmal subarachnoid haemorrhage (aSAH) in western populations is around 9 per 100 000 person-years, which confers to a lifetime risk of around half per cent. Risk factors for aSAH are usually expressed as relative risks and suggest that absolute risks vary considerably according to risk factor profiles, but such estimates are lacking. We aimed to estimate incidence and lifetime risks of aSAH according to risk factor profiles. We used data from 250 patients admitted with aSAH and 574 sex-matched and age-matched controls, who were randomly retrieved from general practitioners files. We determined independent prognostic factors with multivariable logistic regression analyses and assessed discriminatory performance using the area under the receiver operating characteristic curve. Based on the prognostic model we predicted incidences and lifetime risks of aSAH for different risk factor profiles. The four strongest independent predictors for aSAH, namely current smoking (OR 6.0; 95% CI 4.1 to 8.6), a positive family history for aSAH (4.0; 95% CI 2.3 to 7.0), hypertension (2.4; 95% CI 1.5 to 3.8) and hypercholesterolaemia (0.2; 95% CI 0.1 to 0.4), were used in the final prediction model. This model had an area under the receiver operating characteristic curve of 0.73 (95% CI 0.69 to 0.76). Depending on sex, age and the four predictors, the incidence of aSAH ranged from 0.4/100 000 to 298/100 000 person-years and lifetime risk between 0.02% and 7.2%. The incidence and lifetime risk of aSAH in the general population varies widely according to risk factor profiles. Whether persons with high risks benefit from screening should be assessed in cost-effectiveness studies.
Multivariate analysis of 2-DE protein patterns - Practical approaches
DEFF Research Database (Denmark)
Jacobsen, Charlotte; Jacobsen, Susanne; Grove, H.
2007-01-01
Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two...... of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times...
Alternating multivariate trigonometric functions and corresponding Fourier transforms
International Nuclear Information System (INIS)
Klimyk, A U; Patera, J
2008-01-01
We define and study multivariate sine and cosine functions, symmetric with respect to the alternating group A n , which is a subgroup of the permutation (symmetric) group S n . These functions are eigenfunctions of the Laplace operator. They determine Fourier-type transforms. There exist three types of such transforms: expansions into corresponding sine-Fourier and cosine-Fourier series, integral sine-Fourier and cosine-Fourier transforms, and multivariate finite sine and cosine transforms. In all these transforms, alternating multivariate sine and cosine functions are used as a kernel
Scale and shape mixtures of multivariate skew-normal distributions
Arellano-Valle, Reinaldo B.
2018-02-26
We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down the theoretical foundations for subsequent inference with this model. In particular, we study linear transformations, marginal distributions, selection representations, stochastic representations and hierarchical representations. We also describe an EM-type algorithm for maximum likelihood estimation of the parameters of the model and demonstrate its implementation on a wind dataset. Our family of multivariate distributions unifies and extends many existing models of the literature that can be seen as submodels of our proposal.
(Anti)symmetric multivariate exponential functions and corresponding Fourier transforms
International Nuclear Information System (INIS)
Klimyk, A U; Patera, J
2007-01-01
We define and study symmetrized and antisymmetrized multivariate exponential functions. They are defined as determinants and antideterminants of matrices whose entries are exponential functions of one variable. These functions are eigenfunctions of the Laplace operator on the corresponding fundamental domains satisfying certain boundary conditions. To symmetric and antisymmetric multivariate exponential functions there correspond Fourier transforms. There are three types of such Fourier transforms: expansions into the corresponding Fourier series, integral Fourier transforms and multivariate finite Fourier transforms. Eigenfunctions of the integral Fourier transforms are found
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
Disturbance Error Reduction in Multivariable Optimal Control Systems
Directory of Open Access Journals (Sweden)
Ole A. Solheim
1983-01-01
Full Text Available The paper deals with the design of optimal multivariable controllers, using a modified LQR approach. All controllers discussed contain proportional feedback and, in addition, there may be feedforward, integral action or state estimation.
Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS
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
A Newton Algorithm for Multivariate Total Least Squares Problems
Directory of Open Access Journals (Sweden)
WANG Leyang
2016-04-01
Full Text Available In order to improve calculation efficiency of parameter estimation, an algorithm for multivariate weighted total least squares adjustment based on Newton method is derived. The relationship between the solution of this algorithm and that of multivariate weighted total least squares adjustment based on Lagrange multipliers method is analyzed. According to propagation of cofactor, 16 computational formulae of cofactor matrices of multivariate total least squares adjustment are also listed. The new algorithm could solve adjustment problems containing correlation between observation matrix and coefficient matrix. And it can also deal with their stochastic elements and deterministic elements with only one cofactor matrix. The results illustrate that the Newton algorithm for multivariate total least squares problems could be practiced and have higher convergence rate.
Ultrawide Bandwidth Receiver Based on a Multivariate Generalized Gaussian Distribution
Ahmed, Qasim Zeeshan
2015-04-01
Multivariate generalized Gaussian density (MGGD) is used to approximate the multiple access interference (MAI) and additive white Gaussian noise in pulse-based ultrawide bandwidth (UWB) system. The MGGD probability density function (pdf) is shown to be a better approximation of a UWB system as compared to multivariate Gaussian, multivariate Laplacian and multivariate Gaussian-Laplacian mixture (GLM). The similarity between the simulated and the approximated pdf is measured with the help of modified Kullback-Leibler distance (KLD). It is also shown that MGGD has the smallest KLD as compared to Gaussian, Laplacian and GLM densities. A receiver based on the principles of minimum bit error rate is designed for the MGGD pdf. As the requirement is stringent, the adaptive implementation of the receiver is also carried out in this paper. Training sequence of the desired user is the only requirement when implementing the detector adaptively. © 2002-2012 IEEE.
Micro-Raman Imaging for Biology with Multivariate Spectral Analysis
Malvaso, Federica
2015-01-01
. The aim of the following thesis work is to analyze Raman maps related to three pairs of different cells, highlighting differences and similarities through multivariate algorithms. The first pair of analyzed cells are human embryonic stem cells (h
Sunspot Cycle Prediction Using Multivariate Regression and Binary ...
Indian Academy of Sciences (India)
49
Multivariate regression model has been derived based on the available cycles 1 .... The flare index correlates well with various parameters of the solar activity. ...... 32) Sabarinath A and Anilkumar A K 2011 A stochastic prediction model for the.
Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies
Directory of Open Access Journals (Sweden)
Qiong Yang
2012-01-01
Full Text Available Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical or different types of components (e.g., some are continuous and others are categorical. We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.
Fast and Flexible Multivariate Time Series Subsequence Search
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...
Matrix-based introduction to multivariate data analysis
Adachi, Kohei
2016-01-01
This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on ...
Local subdifferentials and multivariational inequalities in Banach and Frechet spaces
Directory of Open Access Journals (Sweden)
Pavlo O. Kasyanov
2008-01-01
Full Text Available Some functional-topological concepts of subdifferential and locally subdifferential maps in Frechet spaces are established. Multivariational inequalities with an operator of the pseudo-monotone type, connected with subdifferential maps, are considered.
Multivariate Cryptography Based on Clipped Hopfield Neural Network.
Wang, Jia; Cheng, Lee-Ming; Su, Tong
2018-02-01
Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in space. The Diffie-Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.
A simplified parsimonious higher order multivariate Markov chain model
Wang, Chao; Yang, Chuan-sheng
2017-09-01
In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.
A tridiagonal parsimonious higher order multivariate Markov chain model
Wang, Chao; Yang, Chuan-sheng
2017-09-01
In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM.
Search for the top quark using multivariate analysis techniques
International Nuclear Information System (INIS)
Bhat, P.C.
1994-08-01
The D0 collaboration is developing top search strategies using multivariate analysis techniques. We report here on applications of the H-matrix method to the eμ channel and neural networks to the e+jets channel
A Scalable Local Algorithm for Distributed Multivariate Regression
National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm can be used for distributed...
Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance
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.
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.
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.
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
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.
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 ...
Input saturation in nonlinear multivariable processes resolved by nonlinear decoupling
Directory of Open Access Journals (Sweden)
Jens G. Balchen
1995-04-01
Full Text Available A new method is presented for the resolution of the problem of input saturation in nonlinear multivariable process control by means of elementary nonlinear decoupling (END. Input saturation can have serious consequences particularly in multivariable control because it may lead to very undesirable system behaviour and quite often system instability. Many authors have searched for systematic techniques for designing multivariable control systems in which saturation may occur in any of the control variables (inputs, manipulated variables. No generally accepted method seems to have been presented so far which gives a solution in closed form. The method of elementary nonlinear decoupling (END can be applied directly to the case of saturation control variables by deriving as many control strategies as there are combinations of saturating control variables. The method is demonstrated by the multivariable control of a simulated Fluidized Catalytic Cracker (FCC with very convincing results.
An Efficient Local Algorithm for Distributed Multivariate Regression
National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm is designed for distributed...
DEFF Research Database (Denmark)
Kjærsgård, Inger Vibeke Holst; Nørrelykke, M.R.; Jessen, Flemming
2006-01-01
Multivariate data analysis has been combined with proteomics to enhance the recovery of information from 2-DE of cod muscle proteins during different storage conditions. Proteins were extracted according to 11 different storage conditions and samples were resolved by 2-DE. Data generated by 2-DE...... was subjected to principal component analysis (PCA) and discriminant partial least squares regression (DPLSR). Applying PCA to 2-DE data revealed the samples to form groups according to frozen storage time, whereas differences due to different storage temperatures or chilled storage in modified atmosphere...... light chain 1, 2 and 3, triose-phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, aldolase A and two ?-actin fragments, and a nuclease diphosphate kinase B fragment to change in concentration, during frozen storage. Application of proteomics, multivariate data analysis and MS/MS to analyse...
A note on inconsistent families of discrete multivariate distributions
Ghosh, Sugata; Dutta, Subhajit; Genton, Marc G.
2017-01-01
We construct a d-dimensional discrete multivariate distribution for which any proper subset of its components belongs to a specific family of distributions. However, the joint d-dimensional distribution fails to belong to that family and in other words, it is ‘inconsistent’ with the distribution of these subsets. We also address preservation of this ‘inconsistency’ property for the symmetric Binomial distribution, and some discrete distributions arising from the multivariate discrete normal distribution.
MULTIVARIATERESIDUES : A Mathematica package for computing multivariate residues
Larsen, Kasper J.; Rietkerk, Robbert
2018-01-01
Multivariate residues appear in many different contexts in theoretical physics and algebraic geometry. In theoretical physics, they for example give the proper definition of generalized-unitarity cuts, and they play a central role in the Grassmannian formulation of the S-matrix by Arkani-Hamed et al. In realistic cases their evaluation can be non-trivial. In this paper we provide a Mathematica package for efficient evaluation of multivariate residues based on methods from computational algebraic geometry.
A note on inconsistent families of discrete multivariate distributions
Ghosh, Sugata
2017-07-05
We construct a d-dimensional discrete multivariate distribution for which any proper subset of its components belongs to a specific family of distributions. However, the joint d-dimensional distribution fails to belong to that family and in other words, it is ‘inconsistent’ with the distribution of these subsets. We also address preservation of this ‘inconsistency’ property for the symmetric Binomial distribution, and some discrete distributions arising from the multivariate discrete normal distribution.
Forecasting multivariate volatility in larger dimensions: some practical issues
Adam E Clements; Ayesha Scott; Annastiina Silvennoinen
2012-01-01
The importance of covariance modelling has long been recognised in the field of portfolio management and large dimensional multivariate problems are increasingly becoming the focus of research. This paper provides a straightforward and commonsense approach toward investigating whether simpler moving average based correlation forecasting methods have equal predictive accuracy as their more complex multivariate GARCH counterparts for large dimensional problems. We find simpler forecasting techn...
DTW-APPROACH FOR UNCORRELATED MULTIVARIATE TIME SERIES IMPUTATION
Phan , Thi-Thu-Hong; Poisson Caillault , Emilie; Bigand , André; Lefebvre , Alain
2017-01-01
International audience; Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper , we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of...
Multivariate Term Structure Models with Level and Heteroskedasticity Effects
DEFF Research Database (Denmark)
Christiansen, Charlotte
2005-01-01
The paper introduces and estimates a multivariate level-GARCH model for the long rate and the term-structure spread where the conditional volatility is proportional to the ãth power of the variable itself (level effects) and the conditional covariance matrix evolves according to a multivariate GA...... and the level model. GARCH effects are more important than level effects. The results are robust to the maturity of the interest rates. Udgivelsesdato: MAY...
Processing data collected from radiometric experiments by multivariate technique
International Nuclear Information System (INIS)
Urbanski, P.; Kowalska, E.; Machaj, B.; Jakowiuk, A.
2005-01-01
Multivariate techniques applied for processing data collected from radiometric experiments can provide more efficient extraction of the information contained in the spectra. Several techniques are considered: (i) multivariate calibration using Partial Least Square Regression and Artificial Neural Network, (ii) standardization of the spectra, (iii) smoothing of collected spectra were autocorrelation function and bootstrap were used for the assessment of the processed data, (iv) image processing using Principal Component Analysis. Application of these techniques is illustrated on examples of some industrial applications. (author)
A Range-Based Multivariate Model for Exchange Rate Volatility
Tims, Ben; Mahieu, Ronald
2003-01-01
textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are interpreted as the underlying currency specific components. Due to the normality of logarithmic volatilities the model can be estimated conveniently with standard Kalman filter techniques. Our resu...
An uncertain journey around the tails of multivariate hydrological distributions
Serinaldi, Francesco
2013-10-01
Moving from univariate to multivariate frequency analysis, this study extends the Klemeš' critique of the widespread belief that the increasingly refined mathematical structures of probability functions increase the accuracy and credibility of the extrapolated upper tails of the fitted distribution models. In particular, we discuss key aspects of multivariate frequency analysis applied to hydrological data such as the selection of multivariate design events (i.e., appropriate subsets or scenarios of multiplets that exhibit the same joint probability to be used in design applications) and the assessment of the corresponding uncertainty. Since these problems are often overlooked or treated separately, and sometimes confused, we attempt to clarify properties, advantages, shortcomings, and reliability of results of frequency analysis. We suggest a selection method of multivariate design events with prescribed joint probability based on simple Monte Carlo simulations that accounts for the uncertainty affecting the inference results and the multivariate extreme quantiles. It is also shown that the exploration of the p-level probability regions of a joint distribution returns a set of events that is a subset of the p-level scenarios resulting from an appropriate assessment of the sampling uncertainty, thus tending to overlook more extreme and potentially dangerous events with the same (uncertain) joint probability. Moreover, a quantitative assessment of the uncertainty of multivariate quantiles is provided by introducing the concept of joint confidence intervals. From an operational point of view, the simulated event sets describing the distribution of the multivariate p-level quantiles can be used to perform multivariate risk analysis under sampling uncertainty. As an example of the practical implications of this study, we analyze two case studies already presented in the literature.
Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion
International Nuclear Information System (INIS)
Garcia-Cabrejo, Oscar; Valocchi, Albert
2014-01-01
Many mathematical and computational models used in engineering produce multivariate output that shows some degree of correlation. However, conventional approaches to Global Sensitivity Analysis (GSA) assume that the output variable is scalar. These approaches are applied on each output variable leading to a large number of sensitivity indices that shows a high degree of redundancy making the interpretation of the results difficult. Two approaches have been proposed for GSA in the case of multivariate output: output decomposition approach [9] and covariance decomposition approach [14] but they are computationally intensive for most practical problems. In this paper, Polynomial Chaos Expansion (PCE) is used for an efficient GSA with multivariate output. The results indicate that PCE allows efficient estimation of the covariance matrix and GSA on the coefficients in the approach defined by Campbell et al. [9], and the development of analytical expressions for the multivariate sensitivity indices defined by Gamboa et al. [14]. - Highlights: • PCE increases computational efficiency in 2 approaches of GSA of multivariate output. • Efficient estimation of covariance matrix of output from coefficients of PCE. • Efficient GSA on coefficients of orthogonal decomposition of the output using PCE. • Analytical expressions of multivariate sensitivity indices from coefficients of PCE
Moore, Hannah E; Pechal, Jennifer L; Benbow, M Eric; Drijfhout, Falko P
2017-05-16
Cuticular hydrocarbons (CHC) have been successfully used in the field of forensic entomology for identifying and ageing forensically important blowfly species, primarily in the larval stages. However in older scenes where all other entomological evidence is no longer present, Calliphoridae puparial cases can often be all that remains and therefore being able to establish the age could give an indication of the PMI. This paper examined the CHCs present in the lipid wax layer of insects, to determine the age of the cases over a period of nine months. The two forensically important species examined were Calliphora vicina and Lucilia sericata. The hydrocarbons were chemically extracted and analysed using Gas Chromatography - Mass Spectrometry. Statistical analysis was then applied in the form of non-metric multidimensional scaling analysis (NMDS), permutational multivariate analysis of variance (PERMANOVA) and random forest models. This study was successful in determining age differences within the empty cases, which to date, has not been establish by any other technique.
McDiarmid, Sue V; Anand, Ravinder; Martz, Karen; Millis, Michael J; Mazariegos, George
2011-07-01
The purpose of this study was to identify significant, independent factors that predicted 6 month patient and graft survival after pediatric liver transplantation. The Studies of Pediatric Liver Transplantation (SPLIT) is a multicenter database established in 1995, of currently more than 4000 US and Canadian children undergoing liver transplantation. Previous published analyses from this data have examined specific factors influencing outcome. This study analyzes a comprehensive range of factors that may influence outcome from the time of listing through the peri- and postoperative period. A total of 42 pre-, peri- and posttransplant variables evaluated in 2982 pediatric recipients of a first liver transplant registered in SPLIT significant at the univariate level were included in multivariate models. In the final model combining all baseline and posttransplant events, posttransplant complications had the highest relative risk of death or graft loss. Reoperation for any cause increased the risk for both patient and graft loss by 11 fold and reoperation exclusive of specific complications by 4 fold. Vascular thromboses, bowel perforation, septicemia, and retransplantation, each independently increased the risk of patient and graft loss by 3 to 4 fold. The only baseline factor with a similarly high relative risk for patient and graft loss was recipient in the intensive care unit (ICU) intubated at transplant. A significant center effect was also found but did not change the impact of the highly significant factors already identified. We conclude that the most significant factors predicting patient and graft loss at 6 months in children listed for transplant are posttransplant surgical complications.
DEFF Research Database (Denmark)
Nielsen, Peter Carøe; Hansen, Hans Nørgaard; Olsen, Flemming Ove
2007-01-01
the obtainable features in direct laser machining as well as heat affected zones in welding processes. This paper describes the development of a measuring unit capable of analysing beam shape and diameter of lasers to be used in manufacturing processes. The analyser is based on the principle of a rotating......The quantitative and qualitative description of laser beam characteristics is important for process implementation and optimisation. In particular, a need for quantitative characterisation of beam diameter was identified when using fibre lasers for micro manufacturing. Here the beam diameter limits...... mechanical wire being swept through the laser beam at varying Z-heights. The reflected signal is analysed and the resulting beam profile determined. The development comprised the design of a flexible fixture capable of providing both rotation and Z-axis movement, control software including data capture...
Contesting Citizenship: Comparative Analyses
DEFF Research Database (Denmark)
Siim, Birte; Squires, Judith
2007-01-01
importance of particularized experiences and multiple ineequality agendas). These developments shape the way citizenship is both practiced and analysed. Mapping neat citizenship modles onto distinct nation-states and evaluating these in relation to formal equality is no longer an adequate approach....... Comparative citizenship analyses need to be considered in relation to multipleinequalities and their intersections and to multiple governance and trans-national organisinf. This, in turn, suggests that comparative citizenship analysis needs to consider new spaces in which struggles for equal citizenship occur...
Clinical patch test data evaluated by multivariate analysis. Danish Contact Dermatitis Group
DEFF Research Database (Denmark)
Christophersen, J; Menné, T; Tanghøj, P
1989-01-01
The aim of the present study was to evaluate the influence of individual explanatory factors, such as sex, age, atopy, test time and presence of diseased skin, on clinical patch test results, by application of multivariate statistical analysis. The study population was 2166 consecutive patients...... patch tested with the standard series of the International Contact Dermatitis Research Group (ICDRG) by members of the Danish Contact Dermatitis Group (DCDG) over a period of 6 months. For the 8 test allergens most often found positive (nickel, fragrance-mix, cobalt, chromate, balsam of Peru, carba......-mix, colophony, and formaldehyde), one or more individual factors were of significance for the risk of being sensitized, except for chromate and formaldehyde. It is concluded that patch test results can be compared only after stratification of the material or by multivariate analysis....
Multivariate factor analysis of Girgentana goat milk composition
Directory of Open Access Journals (Sweden)
Pietro Giaccone
2010-01-01
Full Text Available The interpretation of the several variables that contribute to defining milk quality is difficult due to the high degree of correlation among them. In this case, one of the best methods of statistical processing is factor analysis, which belongs to the multivariate groups; for our study this particular statistical approach was employed. A total of 1485 individual goat milk samples from 117 Girgentana goats, were collected fortnightly from January to July, and analysed for physical and chemical composition, and clotting properties. Milk pH and tritable acidity were within the normal range for fresh goat milk. Morning milk yield resulted 704 ± 323 g with 3.93 ± 1.23% and 3.48±0.38% for fat and protein percentages, respectively. The milk urea content was 43.70 ± 8.28 mg/dl. The clotting ability of Girgentana milk was quite good, with a renneting time equal to 16.96 ± 3.08 minutes, a rate of curd formation of 2.01 ± 1.63 min- utes and a curd firmness of 25.08 ± 7.67 millimetres. Factor analysis was performed by applying axis orthogonal rotation (rotation type VARIMAX; the analysis grouped the milk components into three latent or common factors. The first, which explained 51.2% of the total covariance, was defined as “slow milks”, because it was linked to r and pH. The second latent factor, which explained 36.2% of the total covariance, was defined as “milk yield”, because it is positively correlated to the morning milk yield and to the urea con- tent, whilst negatively correlated to the fat percentage. The third latent factor, which explained 12.6% of the total covari- ance, was defined as “curd firmness,” because it is linked to protein percentage, a30 and titatrable acidity. With the aim of evaluating the influence of environmental effects (stage of kidding, parity and type of kidding, factor scores were anal- ysed with the mixed linear model. Results showed significant effects of the season of
Risico-analyse brandstofpontons
Uijt de Haag P; Post J; LSO
2001-01-01
Voor het bepalen van de risico's van brandstofpontons in een jachthaven is een generieke risico-analyse uitgevoerd. Er is een referentiesysteem gedefinieerd, bestaande uit een betonnen brandstofponton met een relatief grote inhoud en doorzet. Aangenomen is dat de ponton gelegen is in een
Energy Technology Data Exchange (ETDEWEB)
Berry, A; Przybylski, M M; Sumner, I [Science Research Council, Daresbury (UK). Daresbury Lab.
1982-10-01
A fast multichannel analyser (MCA) capable of sampling at a rate of 10/sup 7/ s/sup -1/ has been developed. The instrument is based on an 8 bit parallel encoding analogue to digital converter (ADC) reading into a fast histogramming random access memory (RAM) system, giving 256 channels of 64 k count capacity. The prototype unit is in CAMAC format.
International Nuclear Information System (INIS)
Berry, A.; Przybylski, M.M.; Sumner, I.
1982-01-01
A fast multichannel analyser (MCA) capable of sampling at a rate of 10 7 s -1 has been developed. The instrument is based on an 8 bit parallel encoding analogue to digital converter (ADC) reading into a fast histogramming random access memory (RAM) system, giving 256 channels of 64 k count capacity. The prototype unit is in CAMAC format. (orig.)
Hennigan, Jennifer N.; Grubbs, W. Tandy
2013-01-01
The chemical elements present in the modern periodic table are arranged in terms of atomic numbers and chemical periodicity. Periodicity arises from quantum mechanical limitations on how many electrons can occupy various shells and subshells of an atom. The shell model of the atom predicts that a maximum of 2, 8, 18, and 32 electrons can occupy…
Ettelt, Harold J.
All recent issues of periodicals found which contain indexed book reviews are listed in this compilation from Drake Memorial Library at the New York State University at Brockport. The periodicals are listed by 29 subject headings in this informal guide designed to be used at Drake Library. The number of reviews in the periodical in a recent year…
Multivariate Stable Isotope Analysis to Determine Linkages between Benzocaine Seizures
Kemp, H. F.; Meier-Augenstein, W.; Collins, M.; Salouros, H.; Cunningham, A.; Harrison, M.
2012-04-01
In July 2010, a woman was jailed for nine years in the UK after the prosecution successfully argued that attempting to import a cutting agent was proof of involvement in a conspiracy to supply Cocaine. That landmark ruling provided law enforcement agencies with much greater scope to tackle those involved in this aspect of the drug trade, specifically targeting those importing the likes of benzocaine or lidocaine. Huge quantities of these compounds are imported into the UK and between May and August 2010, four shipments of Benzocaine amounting to more then 4 tons had been seized as part of Operation Kitley, a joint initiative between the UK Border Agency and the Serious Organised Crime Agency (SOCA). By diluting cocaine, traffickers can make it go a lot further for very little cost, leading to huge profits. In recent years, dealers have moved away from inert substances, like sugar and baby milk powder, in favour of active pharmaceutical ingredients (APIs), including anaesthetics like Benzocaine and Lidocaine. Both these mimic the numbing effect of cocaine, and resemble it closely in colour, texture and some chemical behaviours, making it easier to conceal the fact that the drug has been diluted. API cutting agents have helped traffickers to maintain steady supplies in the face of successful interdiction and even expand the market in the UK, particularly to young people aged from their mid teens to early twenties. From importation to street-level, the purity of the drug can be reduced up to a factor of 80 and street level cocaine can have a cocaine content as low as 1%. In view of the increasing use of Benzocaine as cutting agent for Cocaine, a study was carried out to investigate if 2H, 13C, 15N and 18O stable isotope signatures could be used in conjunction with multivariate chemometric data analysis to determine potential linkage between benzocaine exhibits seized from different locations or individuals to assist with investigation and prosecution of drug
Multivariate Methods for Meta-Analysis of Genetic Association Studies.
Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G
2018-01-01
Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.
Drunk driving detection based on classification of multivariate time series.
Li, Zhenlong; Jin, Xue; Zhao, Xiaohua
2015-09-01
This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.
International Nuclear Information System (INIS)
Geiser, Achim
2015-12-01
A variety of possible future analyses of HERA data in the context of the HERA data preservation programme is collected, motivated, and commented. The focus is placed on possible future analyses of the existing ep collider data and their physics scope. Comparisons to the original scope of the HERA pro- gramme are made, and cross references to topics also covered by other participants of the workshop are given. This includes topics on QCD, proton structure, diffraction, jets, hadronic final states, heavy flavours, electroweak physics, and the application of related theory and phenomenology topics like NNLO QCD calculations, low-x related models, nonperturbative QCD aspects, and electroweak radiative corrections. Synergies with other collider programmes are also addressed. In summary, the range of physics topics which can still be uniquely covered using the existing data is very broad and of considerable physics interest, often matching the interest of results from colliders currently in operation. Due to well-established data and MC sets, calibrations, and analysis procedures the manpower and expertise needed for a particular analysis is often very much smaller than that needed for an ongoing experiment. Since centrally funded manpower to carry out such analyses is not available any longer, this contribution not only targets experienced self-funded experimentalists, but also theorists and master-level students who might wish to carry out such an analysis.
Energy Technology Data Exchange (ETDEWEB)
Wilen, C.; Moilanen, A.; Kurkela, E. [VTT Energy, Espoo (Finland). Energy Production Technologies
1996-12-31
The overall objectives of the project `Feasibility of electricity production from biomass by pressurized gasification systems` within the EC Research Programme JOULE II were to evaluate the potential of advanced power production systems based on biomass gasification and to study the technical and economic feasibility of these new processes with different type of biomass feed stocks. This report was prepared as part of this R and D project. The objectives of this task were to perform fuel analyses of potential woody and herbaceous biomasses with specific regard to the gasification properties of the selected feed stocks. The analyses of 15 Scandinavian and European biomass feed stock included density, proximate and ultimate analyses, trace compounds, ash composition and fusion behaviour in oxidizing and reducing atmospheres. The wood-derived fuels, such as whole-tree chips, forest residues, bark and to some extent willow, can be expected to have good gasification properties. Difficulties caused by ash fusion and sintering in straw combustion and gasification are generally known. The ash and alkali metal contents of the European biomasses harvested in Italy resembled those of the Nordic straws, and it is expected that they behave to a great extent as straw in gasification. Any direct relation between the ash fusion behavior (determined according to the standard method) and, for instance, the alkali metal content was not found in the laboratory determinations. A more profound characterisation of the fuels would require gasification experiments in a thermobalance and a PDU (Process development Unit) rig. (orig.) (10 refs.)
A Scheme for Initial Exploratory Data Analysis of Multivariate Image Data
DEFF Research Database (Denmark)
Hilger, Klaus Baggesen; Nielsen, Allan Aasbjerg; Larsen, Rasmus
2001-01-01
A new scheme is proposed for handling initial exploratory analyses of multivariate image data. The method is invariant to linear transformations of the original data and is useful for data fusion of multisource measurements. The scheme includes dimensionality reduction followed by unsupervised...... clustering of the data. A transformation is proposed which maximizes autocorrelation by projection onto subspaces with signal-to-noise ratio dependent variance. We apply the traditional fuzzy c-means algorithm and introduce two additional memberships enhancing the textural awareness of the algorithm. Cluster...
Directory of Open Access Journals (Sweden)
Danique Vervoort
Full Text Available Many tests can crudely quantify age-related mobility decrease but instrumented versions of mobility tests could increase their specificity and sensitivity. The Timed-up-and-Go (TUG test includes several elements that people use in daily life. The test has different transition phases: rise from a chair, walk, 180° turn, walk back, turn, and sit-down on a chair. For this reason the TUG is an often used test to evaluate in a standardized way possible decline in balance and walking ability due to age and or pathology. Using inertial sensors, qualitative information about the performance of the sub-phases can provide more specific information about a decline in balance and walking ability. The first aim of our study was to identify variables extracted from the instrumented timed-up-and-go (iTUG that most effectively distinguished performance differences across age (age 18-75. Second, we determined the discriminative ability of those identified variables to classify a younger (age 18-45 and older age group (age 46-75. From healthy adults (n = 59, trunk accelerations and angular velocities were recorded during iTUG performance. iTUG phases were detected with wavelet-analysis. Using a Partial Least Square (PLS model, from the 72-iTUG variables calculated across phases, those that explained most of the covariance between variables and age were extracted. Subsequently, a PLS-discriminant analysis (DA assessed classification power of the identified iTUG variables to discriminate the age groups. 27 variables, related to turning, walking and the stand-to-sit movement explained 71% of the variation in age. The PLS-DA with these 27 variables showed a sensitivity and specificity of 90% and 85%. Based on this model, the iTUG can accurately distinguish young and older adults. Such data can serve as a reference for pathological aging with respect to a widely used mobility test. Mobility tests like the TUG supplemented with smart technology could be used in clinical practice.
Vervoort, Danique; Vuillerme, Nicolas; Kosse, Nienke; Hortobágyi, Tibor; Lamoth, Claudine J C
2016-01-01
Many tests can crudely quantify age-related mobility decrease but instrumented versions of mobility tests could increase their specificity and sensitivity. The Timed-up-and-Go (TUG) test includes several elements that people use in daily life. The test has different transition phases: rise from a
Gerke, Oliver; Wings, Oliver
2016-01-01
Remains of theropod dinosaurs are very rare in Northern Germany because the area was repeatedly submerged by a shallow epicontinental sea during the Mesozoic. Here, 80 Late Jurassic theropod teeth are described of which the majority were collected over decades from marine carbonates in nowadays abandoned and backfilled quarries of the 19th century. Eighteen different morphotypes (A-R) could be distinguished and 3D models based on micro-CT scans of the best examples of all morphotypes are included as supplements. The teeth were identified with the assistance of discriminant function analysis and cladistic analysis based on updated datamatrices. The results show that a large variety of theropod groups were present in the Late Jurassic of northern Germany. Identified specimens comprise basal Tyrannosauroidea, as well as Allosauroidea, Megalosauroidea cf. Marshosaurus, Megalosauridae cf. Torvosaurus and probably Ceratosauria. The formerly reported presence of Dromaeosauridae in the Late Jurassic of northern Germany could not be confirmed. Some teeth of this study resemble specimens described as pertaining to Carcharodontosauria (morphotype A) and Abelisauridae (morphotype K). This interpretation is however, not supported by discriminant function analysis and cladistic analysis. Two smaller morphotypes (N and Q) differ only in some probably size-related characteristics from larger morphotypes (B and C) and could well represent juveniles of adult specimens. The similarity of the northern German theropods with groups from contemporaneous localities suggests faunal exchange via land-connections in the Late Jurassic between Germany, Portugal and North America.
Directory of Open Access Journals (Sweden)
Oliver Gerke
Full Text Available Remains of theropod dinosaurs are very rare in Northern Germany because the area was repeatedly submerged by a shallow epicontinental sea during the Mesozoic. Here, 80 Late Jurassic theropod teeth are described of which the majority were collected over decades from marine carbonates in nowadays abandoned and backfilled quarries of the 19th century. Eighteen different morphotypes (A-R could be distinguished and 3D models based on micro-CT scans of the best examples of all morphotypes are included as supplements. The teeth were identified with the assistance of discriminant function analysis and cladistic analysis based on updated datamatrices. The results show that a large variety of theropod groups were present in the Late Jurassic of northern Germany. Identified specimens comprise basal Tyrannosauroidea, as well as Allosauroidea, Megalosauroidea cf. Marshosaurus, Megalosauridae cf. Torvosaurus and probably Ceratosauria. The formerly reported presence of Dromaeosauridae in the Late Jurassic of northern Germany could not be confirmed. Some teeth of this study resemble specimens described as pertaining to Carcharodontosauria (morphotype A and Abelisauridae (morphotype K. This interpretation is however, not supported by discriminant function analysis and cladistic analysis. Two smaller morphotypes (N and Q differ only in some probably size-related characteristics from larger morphotypes (B and C and could well represent juveniles of adult specimens. The similarity of the northern German theropods with groups from contemporaneous localities suggests faunal exchange via land-connections in the Late Jurassic between Germany, Portugal and North America.
Lasso and probabilistic inequalities for multivariate point processes
DEFF Research Database (Denmark)
Hansen, Niels Richard; Reynaud-Bouret, Patricia; Rivoirard, Vincent
2015-01-01
Due to its low computational cost, Lasso is an attractive regularization method for high-dimensional statistical settings. In this paper, we consider multivariate counting processes depending on an unknown function parameter to be estimated by linear combinations of a fixed dictionary. To select...... for multivariate Hawkes processes are proven, which allows us to check these assumptions by considering general dictionaries based on histograms, Fourier or wavelet bases. Motivated by problems of neuronal activity inference, we finally carry out a simulation study for multivariate Hawkes processes and compare our...... methodology with the adaptive Lasso procedure proposed by Zou in (J. Amer. Statist. Assoc. 101 (2006) 1418–1429). We observe an excellent behavior of our procedure. We rely on theoretical aspects for the essential question of tuning our methodology. Unlike adaptive Lasso of (J. Amer. Statist. Assoc. 101 (2006...
Multivariate performance reliability prediction in real-time
International Nuclear Information System (INIS)
Lu, S.; Lu, H.; Kolarik, W.J.
2001-01-01
This paper presents a technique for predicting system performance reliability in real-time considering multiple failure modes. The technique includes on-line multivariate monitoring and forecasting of selected performance measures and conditional performance reliability estimates. The performance measures across time are treated as a multivariate time series. A state-space approach is used to model the multivariate time series. Recursive forecasting is performed by adopting Kalman filtering. The predicted mean vectors and covariance matrix of performance measures are used for the assessment of system survival/reliability with respect to the conditional performance reliability. The technique and modeling protocol discussed in this paper provide a means to forecast and evaluate the performance of an individual system in a dynamic environment in real-time. The paper also presents an example to demonstrate the technique
Multivariate phase type distributions - Applications and parameter estimation
DEFF Research Database (Denmark)
Meisch, David
The best known univariate probability distribution is the normal distribution. It is used throughout the literature in a broad field of applications. In cases where it is not sensible to use the normal distribution alternative distributions are at hand and well understood, many of these belonging...... and statistical inference, is the multivariate normal distribution. Unfortunately only little is known about the general class of multivariate phase type distribution. Considering the results concerning parameter estimation and inference theory of univariate phase type distributions, the class of multivariate...... projects and depend on reliable cost estimates. The Successive Principle is a group analysis method primarily used for analyzing medium to large projects in relation to cost or duration. We believe that the mathematical modeling used in the Successive Principle can be improved. We suggested a novel...
Multivariable calculus with Matlab with applications to geometry and physics
Lipsman, Ronald L
2017-01-01
This comprehensive treatment of multivariable calculus focuses on the numerous tools that MATLAB® brings to the subject, as it presents introductions to geometry, mathematical physics, and kinematics. Covering simple calculations with MATLAB®, relevant plots, integration, and optimization, the numerous problem sets encourage practice with newly learned skills that cultivate the reader’s understanding of the material. Significant examples illustrate each topic, and fundamental physical applications such as Kepler’s Law, electromagnetism, fluid flow, and energy estimation are brought to prominent position. Perfect for use as a supplement to any standard multivariable calculus text, a “mathematical methods in physics or engineering” class, for independent study, or even as the class text in an “honors” multivariable calculus course, this textbook will appeal to mathematics, engineering, and physical science students. MATLAB® is tightly integrated into every portion of this book, and its graphical ...
Generalized Enhanced Multivariance Product Representation for Data Partitioning: Constancy Level
International Nuclear Information System (INIS)
Tunga, M. Alper; Demiralp, Metin
2011-01-01
Enhanced Multivariance Product Representation (EMPR) method is used to represent multivariate functions in terms of less-variate structures. The EMPR method extends the HDMR expansion by inserting some additional support functions to increase the quality of the approximants obtained for dominantly or purely multiplicative analytical structures. This work aims to develop the generalized form of the EMPR method to be used in multivariate data partitioning approaches. For this purpose, the Generalized HDMR philosophy is taken into consideration to construct the details of the Generalized EMPR at constancy level as the introductory steps and encouraging results are obtained in data partitioning problems by using our new method. In addition, to examine this performance, a number of numerical implementations with concluding remarks are given at the end of this paper.
Constructing ordinal partition transition networks from multivariate time series.
Zhang, Jiayang; Zhou, Jie; Tang, Ming; Guo, Heng; Small, Michael; Zou, Yong
2017-08-10
A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.
Multivariate methods and forecasting with IBM SPSS statistics
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...
Multivariable controller for a 600 MWe CANDU nuclear power plant
International Nuclear Information System (INIS)
Mensah, S.
1982-11-01
The problems of designing a multivariable regulator for a nuclear power station of the Gentilly-2 type are studied. A reduced model, G2LDM, linearized around steady state operating conditions, is derived from the non-linear model G2SIM. The resulting linear model is described by state-space equations. Good agreement is demonstrated between the transient responses of both models. Properties of G2LDM are assessed by performing controllability and observability tests, cyclicity and rank tests, and eigenanalysis. A comprehensive set of application-orinented algorithms which allow multivariable controller design with closed-loop pole-assignment techniques are implemented in a computer-aided design package via several modules. A general scheme for the implementation of a multivariable controller in G2SIM is designed, and simulation tests show satisfactory performance of the controller [fr
Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO.
Kreibich, Heidi; Botto, Anna; Merz, Bruno; Schröter, Kai
2017-04-01
Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study. © 2016 Society for Risk Analysis.
Chung, Jae Ho; Cho, Seok Hyun; Jeong, Jin Hyeok; Park, Chul Won; Lee, Seung Hwan
2015-09-01
To evaluate clinical characteristics and possible associated factors of idiopathic sudden sensorineural hearing loss (ISSNHL) in children using univariate and multivariate analyses. A retrospective case series with comparisons. From January 2007 to December 2013, medical records of 37 pediatric ISSNHL patients were reviewed to assess hearing recovery rate and examine factors associated with prognosis (gender; side of hearing loss; opposite side hearing loss; treatment onset; presence of vertigo, tinnitus, and ear fullness; initial hearing threshold), using univariate and multivariate analysis, and compare them with 276 adult ISSNHL patients. Pediatric patients comprised only 6.6% of pediatric/adult cases of ISSNHL, and those below 10 years old were only 0.7%. The overall recovery rates (complete and partial) of the pediatric and adult patients were 57.4% and 47.2%, respectively. The complete recovery rate of the pediatric group (46.6%) was higher than that of the adult group (30.8%, P = .040). According to multivariate analysis, absence of tinnitus, later onset of treatment, and higher hearing threshold at initial presentation were associated with a poor prognosis in pediatric ISSNHL. The recovery rate of ISSNHL in pediatric patients is higher than in adults, and the presence of tinnitus and earlier treatment onset is associated with favorable outcomes. 4. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.
Resent state and multivariate analysis of a few juniper forests of baluchistan, pakistan
International Nuclear Information System (INIS)
Ahmed, M.; Siddiqui, M.F.
2015-01-01
Quantitative multivariate investigations were carried out to explore various forms of Juniper trees resulting human disturbances and natural phenomenon. Thirty stands were sampled by point centered quarter method and data were analysed using Wards cluster analysis and Bray-Curtis ordination. On the basis of multivariate analysis eight various forms i.e. healthy, unhealthy, over mature, disturbed, dieback, standing dead, logs and cut stem were recognized. Structural attributes were computed. Highest numbers (130-133 stem ha-1) of logs were recorded from Cautair and Khunk forests. Highest density ha-1 (229 ha-1) of healthy plants was estimated from Tangi Top area while lowest number (24 ha-1) of healthy plants was found from Saraghara area. Multivariate analysis showed five groups in cluster and ordination diagrams. These groups are characterized on the basis of healthy, over mature, disturbed and logged trees of Juniper. Higher number (115, 96, 84, 80 ha-1) of disturbed trees were distributed at Speena Sukher, Srag Kazi, Prang Shella and Tangi Top respectively. Overall density does not show any significant relation with basal area m2 ha-1, degree of slopes and the elevation of the sampling stands. Present study show that each and every Juniper stands are highly disturbed mostly due to human influence, therefore prompt conservational steps should be taken to safe these forests. (author)
Serrano, Pablo E; Cleary, Sean P; Dhani, Neesha; Kim, Peter T W; Greig, Paul D; Leung, Kenneth; Moulton, Carol-Anne; Gallinger, Steven; Wei, Alice C
2015-04-01
Despite reduced perioperative mortality and routine use of adjuvant therapy following pancreatectomy for pancreatic ductal adenocarcinoma (PDAC), improvement in long-term outcome has been difficult to ascertain. This study compares outcomes in patients undergoing resection for PDAC within a single, high-volume academic institution over two sequential time periods. Retrospective review of patients with resected PDAC, in two cohorts: period 1 (P1), 1991-2000; and period 2 (P2), 2001-2010. Univariate and multivariate analyses using the Cox proportional hazards model were performed to determine prognostic factors associated with long-term survival. Survival was evaluated using Kaplan-Meier analyses. A total of 179 pancreatectomies were performed during P1 and 310 during P2. Perioperative mortality was 6.7 % (12/179) in P1 and 1.6 % (5/310) in P2 (p = 0.003). P2 had a greater number of lymph nodes resected (17 [0-50] vs. 7 [0-31]; p P2 (p P2 (p < 0.001). Factors associated with improved long-term survival remain comparable over time. Short- and long-term survival for patients with resected PDAC has improved over time due to decreased perioperative mortality and increased use of adjuvant therapy, although the proportion of 5-year survivors remains small.
Menstruation - painful; Dysmenorrhea; Periods - painful; Cramps - menstrual; Menstrual cramps ... into two groups, depending on the cause: Primary dysmenorrhea Secondary dysmenorrhea Primary dysmenorrhea is menstrual pain that ...
DEFF Research Database (Denmark)
Sarri, Kalliopi
1999-01-01
and their quality was improved considerably toward the end of this period. The profound cultural innovations of the Middle Helladic period were initially interpreted as a result of violent population movement and troubles provoked by the coming of the first Indo-European races. However, this matter does no more...... Helladic period is considered as a period of economic and social decline it was the time during which the mainland features merged with the insular influence, that is all the Aegean elements which led to the creation of the Mycenaean civilization were mixed in a creative way....
Robust methods for multivariate data analysis A1
DEFF Research Database (Denmark)
Frosch, Stina; Von Frese, J.; Bro, Rasmus
2005-01-01
Outliers may hamper proper classical multivariate analysis, and lead to incorrect conclusions. To remedy the problem of outliers, robust methods are developed in statistics and chemometrics. Robust methods reduce or remove the effect of outlying data points and allow the ?good? data to primarily...... determine the result. This article reviews the most commonly used robust multivariate regression and exploratory methods that have appeared since 1996 in the field of chemometrics. Special emphasis is put on the robust versions of chemometric standard tools like PCA and PLS and the corresponding robust...
Multi-variable systems in nuclear power plant
International Nuclear Information System (INIS)
Collins, G.B.; Howell, J.
1982-01-01
Nuclear power plant are complex multi-variable dynamically interactive systems which employ many facets of systems and control theory in their analysis and design. Whole plant mathematical models must be developed and validated and in addition to their obvious role in control system synthesis and design, they are also widely used for operational constraint and plant malfunction analysis. The need for and scope of an integrated power plant control system is discussed and, as a specific example, the design of an integrated feedwater regulator is reviewed. The multi-variable frequency response analysis employed in the design is described in detail. (author)
Banach frames for multivariate alpha-modulation spaces
DEFF Research Database (Denmark)
Borup, Lasse; Nielsen, Morten
2006-01-01
The α-modulation spaces [$Mathematical Term$], form a family of spaces that include the Besov and modulation spaces as special cases. This paper is concerned with construction of Banach frames for α-modulation spaces in the multivariate setting. The frames constructed are unions of independent Ri...... Riesz sequences based on tensor products of univariate brushlet functions, which simplifies the analysis of the full frame. We show that the multivariate α-modulation spaces can be completely characterized by the Banach frames constructed....
Non-fragile multivariable PID controller design via system augmentation
Liu, Jinrong; Lam, James; Shen, Mouquan; Shu, Zhan
2017-07-01
In this paper, the issue of designing non-fragile H∞ multivariable proportional-integral-derivative (PID) controllers with derivative filters is investigated. In order to obtain the controller gains, the original system is associated with an extended system such that the PID controller design can be formulated as a static output-feedback control problem. By taking the system augmentation approach, the conditions with slack matrices for solving the non-fragile H∞ multivariable PID controller gains are established. Based on the results, linear matrix inequality -based iterative algorithms are provided to compute the controller gains. Simulations are conducted to verify the effectiveness of the proposed approaches.
Energy Technology Data Exchange (ETDEWEB)
Lawson, E.M. [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia). Physics Division
1998-03-01
The major use of ANTARES is Accelerator Mass Spectrometry (AMS) with {sup 14}C being the most commonly analysed radioisotope - presently about 35 % of the available beam time on ANTARES is used for {sup 14}C measurements. The accelerator measurements are supported by, and dependent on, a strong sample preparation section. The ANTARES AMS facility supports a wide range of investigations into fields such as global climate change, ice cores, oceanography, dendrochronology, anthropology, and classical and Australian archaeology. Described here are some examples of the ways in which AMS has been applied to support research into the archaeology, prehistory and culture of this continent`s indigenous Aboriginal peoples. (author)
International Nuclear Information System (INIS)
Lawson, E.M.
1998-01-01
The major use of ANTARES is Accelerator Mass Spectrometry (AMS) with 14 C being the most commonly analysed radioisotope - presently about 35 % of the available beam time on ANTARES is used for 14 C measurements. The accelerator measurements are supported by, and dependent on, a strong sample preparation section. The ANTARES AMS facility supports a wide range of investigations into fields such as global climate change, ice cores, oceanography, dendrochronology, anthropology, and classical and Australian archaeology. Described here are some examples of the ways in which AMS has been applied to support research into the archaeology, prehistory and culture of this continent's indigenous Aboriginal peoples. (author)
Digital Repository Service at National Institute of Oceanography (India)
Shirodkar, P.V.; Mesquita, A.; Pradhan, U.K.; Verlekar, X.N.; Babu, M.T.; Vethamony, P.
in the south; Fig.1) using the RCM – 9 MK II current meters, manufactured by Aanderaa Co., Norway. The two locations (13 m and 15 m water depths respectively) were separated by a distance of approximately 10km. Current measurements were carried out for one... and salinity were obtained from different locations using a portable SBE 19 SEACAT Profiler, manufactured by Sea-Bird Electronic, Inc., USA. Vertical profiles were continued for the period 14 -19 April 2007. 2.4. Multivariate statistical analysis a...
Estimating petroleum products demand elasticities in Nigeria. A multivariate cointegration approach
International Nuclear Information System (INIS)
Iwayemi, Akin; Adenikinju, Adeola; Babatunde, M. Adetunji
2010-01-01
This paper formulates and estimates petroleum products demand functions in Nigeria at both aggregative and product level for the period 1977 to 2006 using multivariate cointegration approach. The estimated short and long-run price and income elasticities confirm conventional wisdom that energy consumption responds positively to changes in GDP and negatively to changes in energy price. However, the price and income elasticities of demand varied according to product type. Kerosene and gasoline have relatively high short-run income and price elasticities compared to diesel. Overall, the results show petroleum products to be price and income inelastic. (author)
Estimating petroleum products demand elasticities in Nigeria. A multivariate cointegration approach
Energy Technology Data Exchange (ETDEWEB)
Iwayemi, Akin; Adenikinju, Adeola; Babatunde, M. Adetunji [Department of Economics, University of Ibadan, Ibadan (Nigeria)
2010-01-15
This paper formulates and estimates petroleum products demand functions in Nigeria at both aggregative and product level for the period 1977 to 2006 using multivariate cointegration approach. The estimated short and long-run price and income elasticities confirm conventional wisdom that energy consumption responds positively to changes in GDP and negatively to changes in energy price. However, the price and income elasticities of demand varied according to product type. Kerosene and gasoline have relatively high short-run income and price elasticities compared to diesel. Overall, the results show petroleum products to be price and income inelastic. (author)
A multivariate fall risk assessment model for VHA nursing homes using the minimum data set.
French, Dustin D; Werner, Dennis C; Campbell, Robert R; Powell-Cope, Gail M; Nelson, Audrey L; Rubenstein, Laurence Z; Bulat, Tatjana; Spehar, Andrea M
2007-02-01
The purpose of this study was to develop a multivariate fall risk assessment model beyond the current fall Resident Assessment Protocol (RAP) triggers for nursing home residents using the Minimum Data Set (MDS). Retrospective, clustered secondary data analysis. National Veterans Health Administration (VHA) long-term care nursing homes (N = 136). The study population consisted of 6577 national VHA nursing home residents who had an annual assessment during FY 2005, identified from the MDS, as well as an earlier annual or admission assessment within a 1-year look-back period. A dichotomous multivariate model of nursing home residents coded with a fall on selected fall risk characteristics from the MDS, estimated with general estimation equations (GEE). There were 17 170 assessments corresponding to 6577 long-term care nursing home residents. The increased odds ratio (OR) of being classified as a faller relative to the omitted "dependent" category of activities of daily living (ADL) ranged from OR = 1.35 for "limited" ADL category up to OR = 1.57 for "extensive-2" ADL (P canes, walkers, or crutches, or the use of wheelchairs increases the odds of being a faller (OR = 1.17, P falls in long-term care settings. The model incorporated an ADL index and adjusted for case mix by including only long-term care nursing home residents. The study offers clinicians practical estimates by combining multiple univariate MDS elements in an empirically based, multivariate fall risk assessment model.
The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China
Directory of Open Access Journals (Sweden)
Ling-Ling Pei
2018-03-01
Full Text Available The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China’s pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N model based on the nonlinear least square (NLS method. The Gauss–Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N and the NLS-based TNGM (1, N models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC, and per capita emissions of SO2 and dust, alongside GDP per capita in China during the period 1996–2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N model presents greater precision when forecasting WDPC, SO2 emissions and dust emissions per capita, compared to the traditional GM (1, N model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO2 and dust reduce accordingly.
A Bivariate return period for levee failure monitoring
Isola, M.; Caporali, E.
2017-12-01
Levee breaches are strongly linked with the interaction processes among water, soil and structure, thus many are the factors that affect the breach development. One of the main is the hydraulic load, characterized by intensity and duration, i.e. by the flood event hydrograph. On the magnitude of the hydraulic load is based the levee design, generally without considering the fatigue failure due to the load duration. Moreover, many are the cases in which the levee breach are characterized by flood of magnitude lower than the design one. In order to implement the strategies of flood risk management, we built here a procedure based on a multivariate statistical analysis of flood peak and volume together with the analysis of the past levee failure events. Particularly, in order to define the probability of occurrence of the hydraulic load on a levee, a bivariate copula model is used to obtain the bivariate joint distribution of flood peak and volume. Flood peak is the expression of the load magnitude, while the volume is the expression of the stress over time. We consider the annual flood peak and the relative volume. The volume is given by the hydrograph area between the beginning and the end of event. The beginning of the event is identified as an abrupt rise of the discharge by more than 20%. The end is identified as the point from which the receding limb is characterized by the baseflow, using a nonlinear reservoir algorithm as baseflow separation technique. By this, with the aim to define warning thresholds we consider the past levee failure events and the relative bivariate return period (BTr) compared with the estimation of a traditional univariate model. The discharge data of 30 hydrometric stations of Arno River in Tuscany, Italy, in the period 1995-2016 are analysed. The database of levee failure events, considering for each event the location as well as the failure mode, is also created. The events were registered in the period 2000-2014 by EEA
International Nuclear Information System (INIS)
Takeda, Tatsuoki
1985-01-01
In this article analyses of the MHD stabilities which govern the global behavior of a fusion plasma are described from the viewpoint of the numerical computation. First, we describe the high accuracy calculation of the MHD equilibrium and then the analysis of the linear MHD instability. The former is the basis of the stability analysis and the latter is closely related to the limiting beta value which is a very important theoretical issue of the tokamak research. To attain a stable tokamak plasma with good confinement property it is necessary to control or suppress disruptive instabilities. We, next, describe the nonlinear MHD instabilities which relate with the disruption phenomena. Lastly, we describe vectorization of the MHD codes. The above MHD codes for fusion plasma analyses are relatively simple though very time-consuming and parts of the codes which need a lot of CPU time concentrate on a small portion of the codes, moreover, the codes are usually used by the developers of the codes themselves, which make it comparatively easy to attain a high performance ratio on the vector processor. (author)
DEFF Research Database (Denmark)
Sorokin, Sergey V.
2015-01-01
The talk is concerned with the modelling of wave propagation in and vibration of periodic elastic structures. Although analysis of wave-guide properties of infinite periodic structures is a well establish research subject, some issues have not yet been fully addressed in the literature. The aim o...
Nahlik, Mary Schrodt
2005-01-01
To help make the abstract world of chemistry more concrete eighth-grade students, the author has them create a living periodic table that can be displayed in the classroom or hallway. This display includes information about the elements arranged in the traditional periodic table format, but also includes visual real-world representations of the…
Uncertainty Analyses and Strategy
International Nuclear Information System (INIS)
Kevin Coppersmith
2001-01-01
The DOE identified a variety of uncertainties, arising from different sources, during its assessment of the performance of a potential geologic repository at the Yucca Mountain site. In general, the number and detail of process models developed for the Yucca Mountain site, and the complex coupling among those models, make the direct incorporation of all uncertainties difficult. The DOE has addressed these issues in a number of ways using an approach to uncertainties that is focused on producing a defensible evaluation of the performance of a potential repository. The treatment of uncertainties oriented toward defensible assessments has led to analyses and models with so-called ''conservative'' assumptions and parameter bounds, where conservative implies lower performance than might be demonstrated with a more realistic representation. The varying maturity of the analyses and models, and uneven level of data availability, result in total system level analyses with a mix of realistic and conservative estimates (for both probabilistic representations and single values). That is, some inputs have realistically represented uncertainties, and others are conservatively estimated or bounded. However, this approach is consistent with the ''reasonable assurance'' approach to compliance demonstration, which was called for in the U.S. Nuclear Regulatory Commission's (NRC) proposed 10 CFR Part 63 regulation (64 FR 8640 [DIRS 101680]). A risk analysis that includes conservatism in the inputs will result in conservative risk estimates. Therefore, the approach taken for the Total System Performance Assessment for the Site Recommendation (TSPA-SR) provides a reasonable representation of processes and conservatism for purposes of site recommendation. However, mixing unknown degrees of conservatism in models and parameter representations reduces the transparency of the analysis and makes the development of coherent and consistent probability statements about projected repository
Association of Hormonal Contraception with depression in the postpartum period.
Roberts, Timothy A; Hansen, Shana
2017-12-01
Studies have demonstrated an association between hormonal contraception use with subsequent depression and antidepressant use. This association has not been assessed among postpartum women. This study is a secondary analysis of insurance records from 75,528 postpartum women enrolled in the US military medical system, who delivered between October 2012 and September 2014. Our analyses excluded women who used antidepressants or had a diagnosis of depression in the 24months prior to delivery. We assessed the relationship of hormonal contraception use with subsequent antidepressant use or diagnosis with depression in the first 12months postpartum using Cox proportional hazards regression, with a time dependent covariate measuring exposure to hormonal contraception. Antidepressants were prescribed to 7.8% of women and 5.0% were diagnosed with depression. In multivariable analysis adjusting for demographics, both antidepressant use and diagnosis with depression were associated with: younger age, lower socioeconomic status, and a history of military service. Compared to women with no hormonal contraceptive use, use of etonogestrel containing contraception was associated with a higher risk of antidepressant use (Implant: adjHR:1.22(95%CI:1.06-1.41), pdepression diagnosis (0.56(0.49-0.64), pdepression diagnoses (0.65(0.52-0.82), pdepression diagnosis and antidepressant use in the postpartum period varies with the type of hormonal contraception used. Further research is required to describe the mechanisms of these relationships. Published by Elsevier Inc.
Huber, Annette
2017-01-01
This book casts the theory of periods of algebraic varieties in the natural setting of Madhav Nori’s abelian category of mixed motives. It develops Nori’s approach to mixed motives from scratch, thereby filling an important gap in the literature, and then explains the connection of mixed motives to periods, including a detailed account of the theory of period numbers in the sense of Kontsevich-Zagier and their structural properties. Period numbers are central to number theory and algebraic geometry, and also play an important role in other fields such as mathematical physics. There are long-standing conjectures about their transcendence properties, best understood in the language of cohomology of algebraic varieties or, more generally, motives. Readers of this book will discover that Nori’s unconditional construction of an abelian category of motives (over fields embeddable into the complex numbers) is particularly well suited for this purpose. Notably, Kontsevich's formal period algebra represents a to...
Daftedar Abdelhadi, Raghda Mohamed
Although the Next Generation Science Standards (NGSS) present a detailed set of Science and Engineering Practices, a finer grained representation of the underlying skills is lacking in the standards document. Therefore, it has been reported that teachers are facing challenges deciphering and effectively implementing the standards, especially with regards to the Practices. This analytical study assessed the development of high school chemistry students' (N = 41) inquiry, multivariable causal reasoning skills, and metacognition as a mediator for their development. Inquiry tasks based on concepts of element properties of the periodic table as well as reaction kinetics required students to conduct controlled thought experiments, make inferences, and declare predictions of the level of the outcome variable by coordinating the effects of multiple variables. An embedded mixed methods design was utilized for depth and breadth of understanding. Various sources of data were collected including students' written artifacts, audio recordings of in-depth observational groups and interviews. Data analysis was informed by a conceptual framework formulated around the concepts of coordinating theory and evidence, metacognition, and mental models of multivariable causal reasoning. Results of the study indicated positive change towards conducting controlled experimentation, making valid inferences and justifications. Additionally, significant positive correlation between metastrategic and metacognitive competencies, and sophistication of experimental strategies, signified the central role metacognition played. Finally, lack of consistency in indicating effective variables during the multivariable prediction task pointed towards the fragile mental models of multivariable causal reasoning the students had. Implications for teacher education, science education policy as well as classroom research methods are discussed. Finally, recommendations for developing reform-based chemistry
multivariate approach to the study of aquatic species diversity
African Journals Online (AJOL)
User
2016-12-02
Dec 2, 2016 ... Eigen value of the three variables namely; Temperature, pH and Electrical Conductivity ... affect the stream macroinvertebrates (Fornaroli et al., 2016). ... relation to stream land use activities (Tinotenda et al., ... to rotate the multivariate data cloud and extract the ..... community modeling of species distribution.
Multivariate PAT solutions for biopharmaceutical cultivation: current progress and limitations
Mercier, S.M.; Diepenbroek, B.; Wijffels, R.H.; Streefland, M.
2014-01-01
Increasingly elaborate and voluminous datasets are generated by the (bio)pharmaceutical industry and are a major challenge for application of PAT and QbD principles. Multivariate data analysis (MVDA) is required to delineate relevant process information from large multi-factorial and multi-collinear
Multivariate Receptor Models for Spatially Correlated Multipollutant Data
Jun, Mikyoung
2013-08-01
The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air pollutant data measured at a single monitoring site or measurements of a single pollutant collected at multiple monitoring sites. Despite the growing availability of multipollutant data collected from multiple monitoring sites, there has not yet been any attempt to incorporate spatial dependence that may exist in such data into multivariate receptor modeling. We propose a spatial statistics extension of multivariate receptor models that enables us to incorporate spatial dependence into estimation of source composition profiles and contributions given the prespecified number of sources and the model identification conditions. The proposed method yields more precise estimates of source profiles by accounting for spatial dependence in the estimation. More importantly, it enables predictions of source contributions at unmonitored sites as well as when there are missing values at monitoring sites. The method is illustrated with simulated data and real multipollutant data collected from eight monitoring sites in Harris County, Texas. Supplementary materials for this article, including data and R code for implementing the methods, are available online on the journal web site. © 2013 Copyright Taylor and Francis Group, LLC.
Multivariate Variance Targeting in the BEKK-GARCH Model
DEFF Research Database (Denmark)
Pedersen, Rasmus Søndergaard; Rahbek, Anders
This paper considers asymptotic inference in the multivariate BEKK model based on (co-)variance targeting (VT). By de…nition the VT estimator is a two-step estimator and the theory presented is based on expansions of the modi…ed like- lihood function, or estimating function, corresponding...
Multivariate Variance Targeting in the BEKK-GARCH Model
DEFF Research Database (Denmark)
Pedersen, Rasmus Søndergaard; Rahbek, Anders
2014-01-01
This paper considers asymptotic inference in the multivariate BEKK model based on (co-)variance targeting (VT). By definition the VT estimator is a two-step estimator and the theory presented is based on expansions of the modified likelihood function, or estimating function, corresponding...
Asymptotics of Multivariate Regression with Consecutively Added Dependent Varibles
Raats, V.M.; van der Genugten, B.B.; Moors, J.J.A.
2004-01-01
We consider multivariate regression where new dependent variables are consecutively added during the experiment (or in time).So, viewed at the end of the experiment, the number of observations decreases with each added variable. The explanatory variables are observed throughout.In a previous paper
Multivariate Variance Targeting in the BEKK-GARCH Model
DEFF Research Database (Denmark)
Pedersen, Rasmus Søndergaard; Rahbek, Anders
This paper considers asymptotic inference in the multivariate BEKK model based on (co-)variance targeting (VT). By de…nition the VT estimator is a two-step estimator and the theory presented is based on expansions of the modi…ed likelihood function, or estimating function, corresponding...
Highly Efficient Estimators of Multivariate Location with High Breakdown Point
Lopuhaa, H.P.
1991-01-01
We propose an affine equivariant estimator of multivariate location that combines a high breakdown point and a bounded influence function with high asymptotic efficiency. This proposal is basically a location $M$-estimator based on the observations obtained after scaling with an affine equivariant
A multivariate calibration procedure for the tensammetric determination of detergents
Bos, M.
1989-01-01
A multivariate calibration procedure based on singular value decomposition (SVD) and the Ho-Kashyap algorithm is used for the tensammetric determination of the cationic detergents Hyamine 1622, benzalkonium chloride (BACl), N-cetyl-N,N,N-trimethylammonium bromide (CTABr) and mixtures of CTABr and
Multivariate calibration with least-squares support vector machines.
Thissen, U.M.J.; Ustun, B.; Melssen, W.J.; Buydens, L.M.C.
2004-01-01
This paper proposes the use of least-squares support vector machines (LS-SVMs) as a relatively new nonlinear multivariate calibration method, capable of dealing with ill-posed problems. LS-SVMs are an extension of "traditional" SVMs that have been introduced recently in the field of chemistry and
The masking breakdown point of multivariate outlier identification rules
Becker, Claudia; Gather, Ursula
1997-01-01
In this paper, we consider one-step outlier identifiation rules for multivariate data, generalizing the concept of so-called alpha outlier identifiers, as presented in Davies and Gather (1993) for the case of univariate samples. We investigate, how the finite-sample breakdown points of estimators used in these identification rules influence the masking behaviour of the rules.
MULTIVARIATE CURVE RESOLUTION OF NMR SPECTROSCOPY METABONOMIC DATA
Sandia National Laboratories is working with the EPA to evaluate and develop mathematical tools for analysis of the collected NMR spectroscopy data. Initially, we have focused on the use of Multivariate Curve Resolution (MCR) also known as molecular factor analysis (MFA), a tech...
On the Optimality of Multivariate S-Estimators
Croux, C.; Dehon, C.; Yadine, A.
2010-01-01
In this paper we maximize the efficiency of a multivariate S-estimator under a constraint on the breakdown point. In the linear regression model, it is known that the highest possible efficiency of a maximum breakdown S-estimator is bounded above by 33% for Gaussian errors. We prove the surprising
Estimating the decomposition of predictive information in multivariate systems
Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele
2015-03-01
In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.
Multivariate Meta-Analysis Using Individual Participant Data
Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.
2015-01-01
When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…
A multivariate analysis of factors affecting adoption of improved ...
African Journals Online (AJOL)
This paper analyzes the synergies/tradeoffs involved in the adoption of improved varieties of multiple crops in the mixed crop-livestock production systems of the highlands of Ethiopia A multivariate probit (MVP) model involving a system of four equations for the adoption decision of improved varieties of barley, potatoes, ...
Univariate and Multivariate Specification Search Indices in Covariance Structure Modeling.
Hutchinson, Susan R.
1993-01-01
Simulated population data were used to compare relative performances of the modification index and C. Chou and P. M. Bentler's Lagrange multiplier test (a multivariate generalization of a modification index) for four levels of model misspecification. Both indices failed to recover the true model except at the lowest level of misspecification. (SLD)
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 ...
Differential constraints for bounded recursive identification with multivariate splines
De Visser, C.C.; Chu, Q.P.; Mulder, J.A.
2011-01-01
The ability to perform online model identification for nonlinear systems with unknown dynamics is essential to any adaptive model-based control system. In this paper, a new differential equality constrained recursive least squares estimator for multivariate simplex splines is presented that is able
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...
Displaying an Outlier in Multivariate Data | Gordor | Journal of ...
African Journals Online (AJOL)
... a multivariate data set is proposed. The technique involves the projection of the multidimensional data onto a single dimension called the outlier displaying component. When the observations are plotted on this component the outlier is appreciably revealed. Journal of Applied Science and Technology (JAST), Vol. 4, Nos.
Multivariate operational risk: dependence modelling with Lévy copulas
Böcker, K. and Klüppelberg, C.
2015-01-01
Simultaneous modelling of operational risks occurring in different event type/business line cells poses the challenge for operational risk quantification. Invoking the new concept of L´evy copulas for dependence modelling yields simple approximations of high quality for multivariate operational VAR.
Multivariate Volatility Impulse Response Analysis of GFC News Events
D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert); A.K. Singh (Abhay)
2015-01-01
textabstractThis paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the FTSE 100 index from the
Multivariate Volatility Impulse Response Analysis of GFC News Events
D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert)
2015-01-01
markdownabstract__Abstract__ This paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the