Spatial analysis statistics, visualization, and computational methods

CERN Document Server

Oyana, Tonny J

2015-01-01

An introductory text for the next generation of geospatial analysts and data scientists, Spatial Analysis: Statistics, Visualization, and Computational Methods focuses on the fundamentals of spatial analysis using traditional, contemporary, and computational methods. Outlining both non-spatial and spatial statistical concepts, the authors present practical applications of geospatial data tools, techniques, and strategies in geographic studies. They offer a problem-based learning (PBL) approach to spatial analysis-containing hands-on problem-sets that can be worked out in MS Excel or ArcGIS-as well as detailed illustrations and numerous case studies. The book enables readers to: Identify types and characterize non-spatial and spatial data Demonstrate their competence to explore, visualize, summarize, analyze, optimize, and clearly present statistical data and results Construct testable hypotheses that require inferential statistical analysis Process spatial data, extract explanatory variables, conduct statisti...

Method for statistical data analysis of multivariate observations

CERN Document Server

Gnanadesikan, R

1997-01-01

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

Statistical analysis of 4 types of neck whiplash injuries based on classical meridian theory.

Science.gov (United States)

Chen, Yemeng; Zhao, Yan; Xue, Xiaolin; Li, Hui; Wu, Xiuyan; Zhang, Qunce; Zheng, Xin; Wang, Tianfang

2015-01-01

As one component of the Chinese medicine meridian system, the meridian sinew (Jingjin, (see text), tendino-musculo) is specially described as being for acupuncture treatment of the musculoskeletal system because of its dynamic attributes and tender point correlations. In recent decades, the therapeutic importance of the sinew meridian has become revalued in clinical application. Based on this theory, the authors have established therapeutic strategies of acupuncture treatment in Whiplash-Associated Disorders (WAD) by categorizing four types of neck symptom presentations. The advantage of this new system is to make it much easier for the clinician to find effective acupuncture points. This study attempts to prove the significance of the proposed therapeutic strategies by analyzing data collected from a clinical survey of various WAD using non-supervised statistical methods, such as correlation analysis, factor analysis, and cluster analysis. The clinical survey data have successfully verified discrete characteristics of four neck syndromes, based upon the range of motion (ROM) and tender point location findings. A summary of the relationships among the symptoms of the four neck syndromes has shown the correlation coefficient as having a statistical significance (P < 0.01 or P < 0.05), especially with regard to ROM. Furthermore, factor and cluster analyses resulted in a total of 11 categories of general symptoms, which implies syndrome factors are more related to the Liver, as originally described in classical theory. The hypothesis of meridian sinew syndromes in WAD is clearly supported by the statistical analysis of the clinical trials. This new discovery should be beneficial in improving therapeutic outcomes.

Statistical data analysis using SAS intermediate statistical methods

CERN Document Server

Marasinghe, Mervyn G

2018-01-01

The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitab...

Statistical analysis tolerance using jacobian torsor model based on uncertainty propagation method

Directory of Open Access Journals (Sweden)

W Ghie

2016-04-01

Full Text Available One risk inherent in the use of assembly components is that the behaviourof these components is discovered only at the moment an assembly isbeing carried out. The objective of our work is to enable designers to useknown component tolerances as parameters in models that can be usedto predict properties at the assembly level. In this paper we present astatistical approach to assemblability evaluation, based on tolerance andclearance propagations. This new statistical analysis method for toleranceis based on the Jacobian-Torsor model and the uncertainty measurementapproach. We show how this can be accomplished by modeling thedistribution of manufactured dimensions through applying a probabilitydensity function. By presenting an example we show how statisticaltolerance analysis should be used in the Jacobian-Torsor model. This workis supported by previous efforts aimed at developing a new generation ofcomputational tools for tolerance analysis and synthesis, using theJacobian-Torsor approach. This approach is illustrated on a simple threepartassembly, demonstrating the method’s capability in handling threedimensionalgeometry.

Statistical analysis of brake squeal noise

Science.gov (United States)

Oberst, S.; Lai, J. C. S.

2011-06-01

Despite substantial research efforts applied to the prediction of brake squeal noise since the early 20th century, the mechanisms behind its generation are still not fully understood. Squealing brakes are of significant concern to the automobile industry, mainly because of the costs associated with warranty claims. In order to remedy the problems inherent in designing quieter brakes and, therefore, to understand the mechanisms, a design of experiments study, using a noise dynamometer, was performed by a brake system manufacturer to determine the influence of geometrical parameters (namely, the number and location of slots) of brake pads on brake squeal noise. The experimental results were evaluated with a noise index and ranked for warm and cold brake stops. These data are analysed here using statistical descriptors based on population distributions, and a correlation analysis, to gain greater insight into the functional dependency between the time-averaged friction coefficient as the input and the peak sound pressure level data as the output quantity. The correlation analysis between the time-averaged friction coefficient and peak sound pressure data is performed by applying a semblance analysis and a joint recurrence quantification analysis. Linear measures are compared with complexity measures (nonlinear) based on statistics from the underlying joint recurrence plots. Results show that linear measures cannot be used to rank the noise performance of the four test pad configurations. On the other hand, the ranking of the noise performance of the test pad configurations based on the noise index agrees with that based on nonlinear measures: the higher the nonlinearity between the time-averaged friction coefficient and peak sound pressure, the worse the squeal. These results highlight the nonlinear character of brake squeal and indicate the potential of using nonlinear statistical analysis tools to analyse disc brake squeal.

Statistical inference based on divergence measures

CERN Document Server

Pardo, Leandro

2005-01-01

The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this powerful approach.Statistical Inference Based on Divergence Measures explores classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence. The first two chapters form an overview, from a statistical perspective, of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations, prese...

A novel genome-information content-based statistic for genome-wide association analysis designed for next-generation sequencing data.

Science.gov (United States)

Luo, Li; Zhu, Yun; Xiong, Momiao

2012-06-01

The genome-wide association studies (GWAS) designed for next-generation sequencing data involve testing association of genomic variants, including common, low frequency, and rare variants. The current strategies for association studies are well developed for identifying association of common variants with the common diseases, but may be ill-suited when large amounts of allelic heterogeneity are present in sequence data. Recently, group tests that analyze their collective frequency differences between cases and controls shift the current variant-by-variant analysis paradigm for GWAS of common variants to the collective test of multiple variants in the association analysis of rare variants. However, group tests ignore differences in genetic effects among SNPs at different genomic locations. As an alternative to group tests, we developed a novel genome-information content-based statistics for testing association of the entire allele frequency spectrum of genomic variation with the diseases. To evaluate the performance of the proposed statistics, we use large-scale simulations based on whole genome low coverage pilot data in the 1000 Genomes Project to calculate the type 1 error rates and power of seven alternative statistics: a genome-information content-based statistic, the generalized T(2), collapsing method, multivariate and collapsing (CMC) method, individual χ(2) test, weighted-sum statistic, and variable threshold statistic. Finally, we apply the seven statistics to published resequencing dataset from ANGPTL3, ANGPTL4, ANGPTL5, and ANGPTL6 genes in the Dallas Heart Study. We report that the genome-information content-based statistic has significantly improved type 1 error rates and higher power than the other six statistics in both simulated and empirical datasets.

ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS

Directory of Open Access Journals (Sweden)

Z.-G. Zhou

2016-06-01

Full Text Available Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1 Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST. (2 Forecasting and detecting disturbances in new time series data. (3 Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI and Confidence Levels (CL. The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.

Online Statistical Modeling (Regression Analysis) for Independent Responses

Science.gov (United States)

Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus

2017-06-01

Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.

Information systems development of analysis company financial state based on the expert-statistical approach

Directory of Open Access Journals (Sweden)

M. N. Ivliev

2016-01-01

Full Text Available The work is devoted to methods of analysis the company financial condition, including aggregated ratings. It is proposed to use the generalized solvency and liquidity indicator and the capital structure composite index. Mathematically, the generalized index is a sum of variables-characteristics and weighting factors characterizing the relative importance of individual characteristics composition. It is offered to select the significant features from a set of standard financial ratios, calculated according to enterprises balance sheets. To obtain the weighting factors values it is proposed to use one of the expert statistical approaches, the analytic hierarchy process. The method is as follows: we choose the most important characteristic and after the experts determine the degree of preference for the main feature based on the linguistic scale. Further, matrix of pairwise comparisons based on the assigned ranks is compiled, which characterizes the relative importance of attributes. The required coefficients are determined as elements of a vector of priorities, which is the first vector of the matrix of paired comparisons. The paper proposes a mechanism for finding the fields for rating numbers analysis. In addition, the paper proposes a method for the statistical evaluation of the balance sheets of various companies by calculating the mutual correlation matrices. Based on the considered mathematical methods to determine quantitative characteristics of technical objects financial and economic activities, was developed algorithms, information and software allowing to realize of different systems economic analysis.

Register-based statistics statistical methods for administrative data

CERN Document Server

Wallgren, Anders

2014-01-01

This book provides a comprehensive and up to date treatment of  theory and practical implementation in Register-based statistics. It begins by defining the area, before explaining how to structure such systems, as well as detailing alternative approaches. It explains how to create statistical registers, how to implement quality assurance, and the use of IT systems for register-based statistics. Further to this, clear details are given about the practicalities of implementing such statistical methods, such as protection of privacy and the coordination and coherence of such an undertaking. Thi

No-Reference Video Quality Assessment Based on Statistical Analysis in 3D-DCT Domain.

Science.gov (United States)

Li, Xuelong; Guo, Qun; Lu, Xiaoqiang

2016-05-13

It is an important task to design models for universal no-reference video quality assessment (NR-VQA) in multiple video processing and computer vision applications. However, most existing NR-VQA metrics are designed for specific distortion types which are not often aware in practical applications. A further deficiency is that the spatial and temporal information of videos is hardly considered simultaneously. In this paper, we propose a new NR-VQA metric based on the spatiotemporal natural video statistics (NVS) in 3D discrete cosine transform (3D-DCT) domain. In the proposed method, a set of features are firstly extracted based on the statistical analysis of 3D-DCT coefficients to characterize the spatiotemporal statistics of videos in different views. These features are used to predict the perceived video quality via the efficient linear support vector regression (SVR) model afterwards. The contributions of this paper are: 1) we explore the spatiotemporal statistics of videos in 3DDCT domain which has the inherent spatiotemporal encoding advantage over other widely used 2D transformations; 2) we extract a small set of simple but effective statistical features for video visual quality prediction; 3) the proposed method is universal for multiple types of distortions and robust to different databases. The proposed method is tested on four widely used video databases. Extensive experimental results demonstrate that the proposed method is competitive with the state-of-art NR-VQA metrics and the top-performing FR-VQA and RR-VQA metrics.

A Statistics-Based Material Property Analysis to Support TPS Characterization

Science.gov (United States)

Copeland, Sean R.; Cozmuta, Ioana; Alonso, Juan J.

2012-01-01

Accurate characterization of entry capsule heat shield material properties is a critical component in modeling and simulating Thermal Protection System (TPS) response in a prescribed aerothermal environment. The thermal decomposition of the TPS material during the pyrolysis and charring processes is poorly characterized and typically results in large uncertainties in material properties as inputs for ablation models. These material property uncertainties contribute to large design margins on flight systems and cloud re- construction efforts for data collected during flight and ground testing, making revision to existing models for entry systems more challenging. The analysis presented in this work quantifies how material property uncertainties propagate through an ablation model and guides an experimental test regimen aimed at reducing these uncertainties and characterizing the dependencies between properties in the virgin and charred states for a Phenolic Impregnated Carbon Ablator (PICA) based TPS. A sensitivity analysis identifies how the high-fidelity model behaves in the expected flight environment, while a Monte Carlo based uncertainty propagation strategy is used to quantify the expected spread in the in-depth temperature response of the TPS. An examination of how perturbations to the input probability density functions affect output temperature statistics is accomplished using a Kriging response surface of the high-fidelity model. Simulations are based on capsule configuration and aerothermal environments expected during the Mars Science Laboratory (MSL) entry sequence. We identify and rank primary sources of uncertainty from material properties in a flight-relevant environment, show the dependence on spatial orientation and in-depth location on those uncertainty contributors, and quantify how sensitive the expected results are.

Equipment Maintenance management support system based on statistical analysis of maintenance history data

International Nuclear Information System (INIS)

Shimizu, S.; Ando, Y.; Morioka, T.

1990-01-01

Plant maintenance is recently becoming important with the increase in the number of nuclear power stations and in plant operating time. Various kinds of requirements for plant maintenance, such as countermeasures for equipment degradation and saving maintenance costs while keeping up plant reliability and productivity, are proposed. For this purpose, plant maintenance programs should be improved based on equipment reliability estimated by field data. In order to meet these requirements, it is planned to develop an equipment maintenance management support system for nuclear power plants based on statistical analysis of equipment maintenance history data. The large difference between this proposed new method and current similar methods is to evaluate not only failure data but maintenance data, which includes normal termination data and some degree of degradation or functional disorder data for equipment and parts. So, it is possible to utilize these field data for improving maintenance schedules and to evaluate actual equipment and parts reliability under the current maintenance schedule. In the present paper, the authors show the objectives of this system, an outline of this system and its functions, and the basic technique for collecting and managing of maintenance history data on statistical analysis. It is shown, from the results of feasibility tests using simulation data of maintenance history, that this system has the ability to provide useful information for maintenance and the design enhancement

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

Science.gov (United States)

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

2017-10-13

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

Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres

Science.gov (United States)

Raffelt, David A.; Smith, Robert E.; Ridgway, Gerard R.; Tournier, J-Donald; Vaughan, David N.; Rose, Stephen; Henderson, Robert; Connelly, Alan

2015-01-01

In brain regions containing crossing fibre bundles, voxel-average diffusion MRI measures such as fractional anisotropy (FA) are difficult to interpret, and lack within-voxel single fibre population specificity. Recent work has focused on the development of more interpretable quantitative measures that can be associated with a specific fibre population within a voxel containing crossing fibres (herein we use fixel to refer to a specific fibre population within a single voxel). Unfortunately, traditional 3D methods for smoothing and cluster-based statistical inference cannot be used for voxel-based analysis of these measures, since the local neighbourhood for smoothing and cluster formation can be ambiguous when adjacent voxels may have different numbers of fixels, or ill-defined when they belong to different tracts. Here we introduce a novel statistical method to perform whole-brain fixel-based analysis called connectivity-based fixel enhancement (CFE). CFE uses probabilistic tractography to identify structurally connected fixels that are likely to share underlying anatomy and pathology. Probabilistic connectivity information is then used for tract-specific smoothing (prior to the statistical analysis) and enhancement of the statistical map (using a threshold-free cluster enhancement-like approach). To investigate the characteristics of the CFE method, we assessed sensitivity and specificity using a large number of combinations of CFE enhancement parameters and smoothing extents, using simulated pathology generated with a range of test-statistic signal-to-noise ratios in five different white matter regions (chosen to cover a broad range of fibre bundle features). The results suggest that CFE input parameters are relatively insensitive to the characteristics of the simulated pathology. We therefore recommend a single set of CFE parameters that should give near optimal results in future studies where the group effect is unknown. We then demonstrate the proposed method

Beginning statistics with data analysis

CERN Document Server

Mosteller, Frederick; Rourke, Robert EK

2013-01-01

This introduction to the world of statistics covers exploratory data analysis, methods for collecting data, formal statistical inference, and techniques of regression and analysis of variance. 1983 edition.

Application of Ontology Technology in Health Statistic Data Analysis.

Science.gov (United States)

Guo, Minjiang; Hu, Hongpu; Lei, Xingyun

2017-01-01

Research Purpose: establish health management ontology for analysis of health statistic data. Proposed Methods: this paper established health management ontology based on the analysis of the concepts in China Health Statistics Yearbook, and used protégé to define the syntactic and semantic structure of health statistical data. six classes of top-level ontology concepts and their subclasses had been extracted and the object properties and data properties were defined to establish the construction of these classes. By ontology instantiation, we can integrate multi-source heterogeneous data and enable administrators to have an overall understanding and analysis of the health statistic data. ontology technology provides a comprehensive and unified information integration structure of the health management domain and lays a foundation for the efficient analysis of multi-source and heterogeneous health system management data and enhancement of the management efficiency.

Anomalous heat transfer modes of nanofluids: a review based on statistical analysis

Science.gov (United States)

2011-01-01

This paper contains the results of a concise statistical review analysis of a large amount of publications regarding the anomalous heat transfer modes of nanofluids. The application of nanofluids as coolants is a novel practise with no established physical foundations explaining the observed anomalous heat transfer. As a consequence, traditional methods of performing a literature review may not be adequate in presenting objectively the results representing the bulk of the available literature. The current literature review analysis aims to resolve the problems faced by researchers in the past by employing an unbiased statistical analysis to present and reveal the current trends and general belief of the scientific community regarding the anomalous heat transfer modes of nanofluids. The thermal performance analysis indicated that statistically there exists a variable enhancement for conduction, convection/mixed heat transfer, pool boiling heat transfer and critical heat flux modes. The most popular proposed mechanisms in the literature to explain heat transfer in nanofluids are revealed, as well as possible trends between nanofluid properties and thermal performance. The review also suggests future experimentation to provide more conclusive answers to the control mechanisms and influential parameters of heat transfer in nanofluids. PMID:21711932

Anomalous heat transfer modes of nanofluids: a review based on statistical analysis

Science.gov (United States)

Sergis, Antonis; Hardalupas, Yannis

2011-05-01

This paper contains the results of a concise statistical review analysis of a large amount of publications regarding the anomalous heat transfer modes of nanofluids. The application of nanofluids as coolants is a novel practise with no established physical foundations explaining the observed anomalous heat transfer. As a consequence, traditional methods of performing a literature review may not be adequate in presenting objectively the results representing the bulk of the available literature. The current literature review analysis aims to resolve the problems faced by researchers in the past by employing an unbiased statistical analysis to present and reveal the current trends and general belief of the scientific community regarding the anomalous heat transfer modes of nanofluids. The thermal performance analysis indicated that statistically there exists a variable enhancement for conduction, convection/mixed heat transfer, pool boiling heat transfer and critical heat flux modes. The most popular proposed mechanisms in the literature to explain heat transfer in nanofluids are revealed, as well as possible trends between nanofluid properties and thermal performance. The review also suggests future experimentation to provide more conclusive answers to the control mechanisms and influential parameters of heat transfer in nanofluids.

Anomalous heat transfer modes of nanofluids: a review based on statistical analysis

Directory of Open Access Journals (Sweden)

Sergis Antonis

2011-01-01

Full Text Available Abstract This paper contains the results of a concise statistical review analysis of a large amount of publications regarding the anomalous heat transfer modes of nanofluids. The application of nanofluids as coolants is a novel practise with no established physical foundations explaining the observed anomalous heat transfer. As a consequence, traditional methods of performing a literature review may not be adequate in presenting objectively the results representing the bulk of the available literature. The current literature review analysis aims to resolve the problems faced by researchers in the past by employing an unbiased statistical analysis to present and reveal the current trends and general belief of the scientific community regarding the anomalous heat transfer modes of nanofluids. The thermal performance analysis indicated that statistically there exists a variable enhancement for conduction, convection/mixed heat transfer, pool boiling heat transfer and critical heat flux modes. The most popular proposed mechanisms in the literature to explain heat transfer in nanofluids are revealed, as well as possible trends between nanofluid properties and thermal performance. The review also suggests future experimentation to provide more conclusive answers to the control mechanisms and influential parameters of heat transfer in nanofluids.

Research design and statistical analysis

CERN Document Server

Myers, Jerome L; Lorch Jr, Robert F

2013-01-01

Research Design and Statistical Analysis provides comprehensive coverage of the design principles and statistical concepts necessary to make sense of real data.  The book's goal is to provide a strong conceptual foundation to enable readers to generalize concepts to new research situations.  Emphasis is placed on the underlying logic and assumptions of the analysis and what it tells the researcher, the limitations of the analysis, and the consequences of violating assumptions.  Sampling, design efficiency, and statistical models are emphasized throughout. As per APA recommendations

Statistical data analysis handbook

National Research Council Canada - National Science Library

Wall, Francis J

1986-01-01

It must be emphasized that this is not a text book on statistics. Instead it is a working tool that presents data analysis in clear, concise terms which can be readily understood even by those without formal training in statistics...

Multivariate statistical analysis a high-dimensional approach

CERN Document Server

Serdobolskii, V

2000-01-01

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

Statistical Power in Meta-Analysis

Science.gov (United States)

Liu, Jin

2015-01-01

Statistical power is important in a meta-analysis study, although few studies have examined the performance of simulated power in meta-analysis. The purpose of this study is to inform researchers about statistical power estimation on two sample mean difference test under different situations: (1) the discrepancy between the analytical power and…

Integrating eQTL data with GWAS summary statistics in pathway-based analysis with application to schizophrenia.

Science.gov (United States)

Wu, Chong; Pan, Wei

2018-04-01

Many genetic variants affect complex traits through gene expression, which can be exploited to boost statistical power and enhance interpretation in genome-wide association studies (GWASs) as demonstrated by the transcriptome-wide association study (TWAS) approach. Furthermore, due to polygenic inheritance, a complex trait is often affected by multiple genes with similar functions as annotated in gene pathways. Here, we extend TWAS from gene-based analysis to pathway-based analysis: we integrate public pathway collections, expression quantitative trait locus (eQTL) data and GWAS summary association statistics (or GWAS individual-level data) to identify gene pathways associated with complex traits. The basic idea is to weight the SNPs of the genes in a pathway based on their estimated cis-effects on gene expression, then adaptively test for association of the pathway with a GWAS trait by effectively aggregating possibly weak association signals across the genes in the pathway. The P values can be calculated analytically and thus fast. We applied our proposed test with the KEGG and GO pathways to two schizophrenia (SCZ) GWAS summary association data sets, denoted by SCZ1 and SCZ2 with about 20,000 and 150,000 subjects, respectively. Most of the significant pathways identified by analyzing the SCZ1 data were reproduced by the SCZ2 data. Importantly, we identified 15 novel pathways associated with SCZ, such as GABA receptor complex (GO:1902710), which could not be uncovered by the standard single SNP-based analysis or gene-based TWAS. The newly identified pathways may help us gain insights into the biological mechanism underlying SCZ. Our results showcase the power of incorporating gene expression information and gene functional annotations into pathway-based association testing for GWAS. © 2018 WILEY PERIODICALS, INC.

Software development for statistical handling of dosimetric and epidemiological data base

International Nuclear Information System (INIS)

Amaro, M.

1990-01-01

The dose records from different groups of occupationally exposed workers are available in a computerized data base whose main purpose is the individual dose follow-up. Apart from this objective, such a dosimetric data base can be useful to obtain statistical analysis. The type of statistical n formation that can be extracted from the data base may aim to attain mainly two kinds of objectives: - Individual and collective dose distributions and statistics. -Epidemiological statistics. The report describes the software developed to obtain the statistical reports required by the Regulatory Body, as well as any other type of dose distributions or statistics to be included in epidemiological studies A Users Guide for the operators who handle this software package, and the codes listings, are also included in the report. (Author) 2 refs

Software development for statistical handling of dosimetric and epidemiological data base

International Nuclear Information System (INIS)

Amaro, M.

1990-01-01

The dose records from different group of occupationally exposed workers are available in a computerized data base whose main purpose is the individual dose follow-up. Apart from this objective, such a dosimetric data base can be useful to obtain statistical analysis. The type of statistical information that can be extracted from the data base may aim to attain mainly two kinds of obsectives: - Individual and collective dose distributions and statistics. - Epidemiological statistics. The report describes the software developed to obtain the statistical reports required by the Regulatory Body, as well as any other type of dose distributions or statistics to be included in epidsemiological studies. A Users Guide for the operators who handle this sofware package, and the codes listings, are also included in the report. (Author)

What type of statistical model to choose for the analysis of radioimmunoassays

International Nuclear Information System (INIS)

Huet, S.

1984-01-01

The current techniques used for statistical analysis of radioimmunoassays are not very satisfactory for either the statistician or the biologist. They are based on an attempt to make the response curve linear to avoid complicated computations. The present article shows that this practice has considerable effects (often neglected) on the statistical assumptions which must be formulated. A more strict analysis is proposed by applying the four-parameter logistic model. The advantages of this method are: the statistical assumptions formulated are based on observed data, and the model can be applied to almost all radioimmunoassays [fr

Signal processing and statistical analysis of spaced-based measurements

International Nuclear Information System (INIS)

Iranpour, K.

1996-05-01

The reports deals with data obtained by the ROSE rocket project. This project was designed to investigate the low altitude auroral instabilities in the electrojet region. The spectral and statistical analyses indicate the existence of unstable waves in the ionized gas in the region. An experimentally obtained dispersion relation for these waves were established. It was demonstrated that the characteristic phase velocities are much lower than what is expected from the standard theoretical results. This analysis of the ROSE data indicate the cascading of energy from lower to higher frequencies. 44 refs., 54 figs

Security of statistical data bases: invasion of privacy through attribute correlational modeling

Energy Technology Data Exchange (ETDEWEB)

Palley, M.A.

1985-01-01

This study develops, defines, and applies a statistical technique for the compromise of confidential information in a statistical data base. Attribute Correlational Modeling (ACM) recognizes that the information contained in a statistical data base represents real world statistical phenomena. As such, ACM assumes correlational behavior among the database attributes. ACM proceeds to compromise confidential information through creation of a regression model, where the confidential attribute is treated as the dependent variable. The typical statistical data base may preclude the direct application of regression. In this scenario, the research introduces the notion of a synthetic data base, created through legitimate queries of the actual data base, and through proportional random variation of responses to these queries. The synthetic data base is constructed to resemble the actual data base as closely as possible in a statistical sense. ACM then applies regression analysis to the synthetic data base, and utilizes the derived model to estimate confidential information in the actual database.

Degree-based statistic and center persistency for brain connectivity analysis.

Science.gov (United States)

Yoo, Kwangsun; Lee, Peter; Chung, Moo K; Sohn, William S; Chung, Sun Ju; Na, Duk L; Ju, Daheen; Jeong, Yong

2017-01-01

Brain connectivity analyses have been widely performed to investigate the organization and functioning of the brain, or to observe changes in neurological or psychiatric conditions. However, connectivity analysis inevitably introduces the problem of mass-univariate hypothesis testing. Although, several cluster-wise correction methods have been suggested to address this problem and shown to provide high sensitivity, these approaches fundamentally have two drawbacks: the lack of spatial specificity (localization power) and the arbitrariness of an initial cluster-forming threshold. In this study, we propose a novel method, degree-based statistic (DBS), performing cluster-wise inference. DBS is designed to overcome the above-mentioned two shortcomings. From a network perspective, a few brain regions are of critical importance and considered to play pivotal roles in network integration. Regarding this notion, DBS defines a cluster as a set of edges of which one ending node is shared. This definition enables the efficient detection of clusters and their center nodes. Furthermore, a new measure of a cluster, center persistency (CP) was introduced. The efficiency of DBS with a known "ground truth" simulation was demonstrated. Then they applied DBS to two experimental datasets and showed that DBS successfully detects the persistent clusters. In conclusion, by adopting a graph theoretical concept of degrees and borrowing the concept of persistence from algebraic topology, DBS could sensitively identify clusters with centric nodes that would play pivotal roles in an effect of interest. DBS is potentially widely applicable to variable cognitive or clinical situations and allows us to obtain statistically reliable and easily interpretable results. Hum Brain Mapp 38:165-181, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Analysis of the interannual variability of tropical cyclones striking the California coast based on statistical downscaling

Science.gov (United States)

Mendez, F. J.; Rueda, A.; Barnard, P.; Mori, N.; Nakajo, S.; Espejo, A.; del Jesus, M.; Diez Sierra, J.; Cofino, A. S.; Camus, P.

2016-02-01

Hurricanes hitting California have a very low ocurrence probability due to typically cool ocean temperature and westward tracks. However, damages associated to these improbable events would be dramatic in Southern California and understanding the oceanographic and atmospheric drivers is of paramount importance for coastal risk management for present and future climates. A statistical analysis of the historical events is very difficult due to the limited resolution of atmospheric and oceanographic forcing data available. In this work, we propose a combination of: (a) statistical downscaling methods (Espejo et al, 2015); and (b) a synthetic stochastic tropical cyclone (TC) model (Nakajo et al, 2014). To build the statistical downscaling model, Y=f(X), we apply a combination of principal component analysis and the k-means classification algorithm to find representative patterns from a potential TC index derived from large-scale SST fields in Eastern Central Pacific (predictor X) and the associated tropical cyclone ocurrence (predictand Y). SST data comes from NOAA Extended Reconstructed SST V3b providing information from 1854 to 2013 on a 2.0 degree x 2.0 degree global grid. As data for the historical occurrence and paths of tropical cycloneas are scarce, we apply a stochastic TC model which is based on a Monte Carlo simulation of the joint distribution of track, minimum sea level pressure and translation speed of the historical events in the Eastern Central Pacific Ocean. Results will show the ability of the approach to explain seasonal-to-interannual variability of the predictor X, which is clearly related to El Niño Southern Oscillation. References Espejo, A., Méndez, F.J., Diez, J., Medina, R., Al-Yahyai, S. (2015) Seasonal probabilistic forecasting of tropical cyclone activity in the North Indian Ocean, Journal of Flood Risk Management, DOI: 10.1111/jfr3.12197 Nakajo, S., N. Mori, T. Yasuda, and H. Mase (2014) Global Stochastic Tropical Cyclone Model Based on

Statistical methods for astronomical data analysis

CERN Document Server

Chattopadhyay, Asis Kumar

2014-01-01

This book introduces “Astrostatistics” as a subject in its own right with rewarding examples, including work by the authors with galaxy and Gamma Ray Burst data to engage the reader. This includes a comprehensive blending of Astrophysics and Statistics. The first chapter’s coverage of preliminary concepts and terminologies for astronomical phenomenon will appeal to both Statistics and Astrophysics readers as helpful context. Statistics concepts covered in the book provide a methodological framework. A unique feature is the inclusion of different possible sources of astronomical data, as well as software packages for converting the raw data into appropriate forms for data analysis. Readers can then use the appropriate statistical packages for their particular data analysis needs. The ideas of statistical inference discussed in the book help readers determine how to apply statistical tests. The authors cover different applications of statistical techniques already developed or specifically introduced for ...

Marine ecology conditions at Weda Bay, North Maluku based on statistical analysis on distribution of recent foraminifera

Directory of Open Access Journals (Sweden)

Kurniasih Anis

2017-01-01

Full Text Available Analysis of foraminifera in geology,usually being used to find the age of rocks/ sediments and depositional environment. In this study, recent foraminifera was used not only to determinethe sedimentary environment,but also to estimate the ecological condition of the water through a statistical approach.Analysis was performed quantitatively in 10 surface seabed sediment samples in Weda Bay North Maluku. The analysis includes dominance (Sympson Index, diversity and evenness (Shannon Index, and the ratio of planktonic -benthic. The results were shown in the plotting diagram of M-R-T (Miliolid-Rotalid-Textularid to determine the depositional environment. Quantitative analysis was performed using Past software (paleontological version Statistic 1:29.The analysis result showed there was no domination of certain taxon with a moderate degree of evenness and stable communities and considerably a moderate diversity. The results of this analysis indicated that research area had a stable water conditions with the optimum level of carbonate content, oxygen supply, salinity, and temperature. The ratio of planktonic and benthic indicate the relative depth, which was deeper the water increased the percentage of planktonic foraminifera. Based on M-R-T diagram showed the distribution of sediment deposited on exposed carbonate (carbonate platform environment with normal saline.

Parametric analysis of the statistical model of the stick-slip process

Science.gov (United States)

Lima, Roberta; Sampaio, Rubens

2017-06-01

In this paper it is performed a parametric analysis of the statistical model of the response of a dry-friction oscillator. The oscillator is a spring-mass system which moves over a base with a rough surface. Due to this roughness, the mass is subject to a dry-frictional force modeled as a Coulomb friction. The system is stochastically excited by an imposed bang-bang base motion. The base velocity is modeled by a Poisson process for which a probabilistic model is fully specified. The excitation induces in the system stochastic stick-slip oscillations. The system response is composed by a random sequence alternating stick and slip-modes. With realizations of the system, a statistical model is constructed for this sequence. In this statistical model, the variables of interest of the sequence are modeled as random variables, as for example, the number of time intervals in which stick or slip occur, the instants at which they begin, and their duration. Samples of the system response are computed by integration of the dynamic equation of the system using independent samples of the base motion. Statistics and histograms of the random variables which characterize the stick-slip process are estimated for the generated samples. The objective of the paper is to analyze how these estimated statistics and histograms vary with the system parameters, i.e., to make a parametric analysis of the statistical model of the stick-slip process.

Analysis of photon statistics with Silicon Photomultiplier

International Nuclear Information System (INIS)

D'Ascenzo, N.; Saveliev, V.; Wang, L.; Xie, Q.

2015-01-01

The Silicon Photomultiplier (SiPM) is a novel silicon-based photodetector, which represents the modern perspective of low photon flux detection. The aim of this paper is to provide an introduction on the statistical analysis methods needed to understand and estimate in quantitative way the correct features and description of the response of the SiPM to a coherent source of light

Conjunction analysis and propositional logic in fMRI data analysis using Bayesian statistics.

Science.gov (United States)

Rudert, Thomas; Lohmann, Gabriele

2008-12-01

To evaluate logical expressions over different effects in data analyses using the general linear model (GLM) and to evaluate logical expressions over different posterior probability maps (PPMs). In functional magnetic resonance imaging (fMRI) data analysis, the GLM was applied to estimate unknown regression parameters. Based on the GLM, Bayesian statistics can be used to determine the probability of conjunction, disjunction, implication, or any other arbitrary logical expression over different effects or contrast. For second-level inferences, PPMs from individual sessions or subjects are utilized. These PPMs can be combined to a logical expression and its probability can be computed. The methods proposed in this article are applied to data from a STROOP experiment and the methods are compared to conjunction analysis approaches for test-statistics. The combination of Bayesian statistics with propositional logic provides a new approach for data analyses in fMRI. Two different methods are introduced for propositional logic: the first for analyses using the GLM and the second for common inferences about different probability maps. The methods introduced extend the idea of conjunction analysis to a full propositional logic and adapt it from test-statistics to Bayesian statistics. The new approaches allow inferences that are not possible with known standard methods in fMRI. (c) 2008 Wiley-Liss, Inc.

DHLAS: A web-based information system for statistical genetic analysis of HLA population data.

Science.gov (United States)

Thriskos, P; Zintzaras, E; Germenis, A

2007-03-01

DHLAS (database HLA system) is a user-friendly, web-based information system for the analysis of human leukocyte antigens (HLA) data from population studies. DHLAS has been developed using JAVA and the R system, it runs on a Java Virtual Machine and its user-interface is web-based powered by the servlet engine TOMCAT. It utilizes STRUTS, a Model-View-Controller framework and uses several GNU packages to perform several of its tasks. The database engine it relies upon for fast access is MySQL, but others can be used a well. The system estimates metrics, performs statistical testing and produces graphs required for HLA population studies: (i) Hardy-Weinberg equilibrium (calculated using both asymptotic and exact tests), (ii) genetics distances (Euclidian or Nei), (iii) phylogenetic trees using the unweighted pair group method with averages and neigbor-joining method, (iv) linkage disequilibrium (pairwise and overall, including variance estimations), (v) haplotype frequencies (estimate using the expectation-maximization algorithm) and (vi) discriminant analysis. The main merit of DHLAS is the incorporation of a database, thus, the data can be stored and manipulated along with integrated genetic data analysis procedures. In addition, it has an open architecture allowing the inclusion of other functions and procedures.

Determination of the minimum size of a statistical representative volume element from a fibre-reinforced composite based on point pattern statistics

DEFF Research Database (Denmark)

Hansen, Jens Zangenberg; Brøndsted, Povl

2013-01-01

In a previous study, Trias et al. [1] determined the minimum size of a statistical representative volume element (SRVE) of a unidirectional fibre-reinforced composite primarily based on numerical analyses of the stress/strain field. In continuation of this, the present study determines the minimu...... size of an SRVE based on a statistical analysis on the spatial statistics of the fibre packing patterns found in genuine laminates, and those generated numerically using a microstructure generator. © 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved....

Strategies for improving the Voxel-based statistical analysis for animal PET studies: assessment of cerebral glucose metabolism in cat deafness model

International Nuclear Information System (INIS)

Kim, Jin Su; Lee, Jae Sung; Park, Min Hyun; Kang, Hye Jin; Im, Ki Chun; Moon, Dae Hyuk; Lim, Sang Moo; Oh, Seung Ha; Lee, Dong Soo

2007-01-01

In imaging studies of the human brain, voxel-based statistical analysis method was widely used, since these methods were originally developed for the analysis of the human brain data, they are not optimal for the animal brain data. The aim of this study is to optimize the procedures for the 3D voxel-based statistical analysis of cat FDG PET brain images. A microPET Focus 120 scanner was used. Eight cats underwent FDG PET scans twice before and after inducing the deafness. Only the brain and adjacent regions were extracted from each data set by manual masking. Individual PET image at normal and deaf state was realigned to each other to remove the confounding effects by the different spatial normalization parameters on the results of statistical analyses. Distance between the sampling points on the reference image and kernel size of Gaussian filter applied to the images before estimating the realignment parameters were adjusted to 0.5 mm and 2 mm. Both data was then spatial normalized onto study-specific cat brain template. Spatially normalized PET data were smoothed and voxel-based paired t-test was performed. Cerebral glucose metabolism decreased significantly after the loss of hearing capability in parietal lobes, postcentral gyri, STG, MTG, lTG, and IC at both hemisphere and left SC (FDR corrected P < 0.05, k=50). Cerebral glucose metabolism in deaf cats was found to be significantly higher than in controls in the right cingulate (FDR corrected P < 0.05, k=50). The ROI analysis also showed significant reduction of glucose metabolism in the same areas as in the SPM analysis, except for some regions (P < 0.05). Method for the voxel-based analysis of cat brain PET data was optimized for analysis of cat brain PET. This result was also confirmed by ROI analysis. The results obtained demonstrated the high localization accuracy and specificity of the developed method, and were found to be useful for examining cerebral glucose metabolism in a cat cortical deafness model

Strategies for improving the Voxel-based statistical analysis for animal PET studies: assessment of cerebral glucose metabolism in cat deafness model

Energy Technology Data Exchange (ETDEWEB)

Kim, Jin Su; Lee, Jae Sung; Park, Min Hyun; Kang, Hye Jin; Im, Ki Chun; Moon, Dae Hyuk; Lim, Sang Moo; Oh, Seung Ha; Lee, Dong Soo [Seoul National Univ. College of Medicine, Seoul (Korea, Republic of)

2007-07-01

In imaging studies of the human brain, voxel-based statistical analysis method was widely used, since these methods were originally developed for the analysis of the human brain data, they are not optimal for the animal brain data. The aim of this study is to optimize the procedures for the 3D voxel-based statistical analysis of cat FDG PET brain images. A microPET Focus 120 scanner was used. Eight cats underwent FDG PET scans twice before and after inducing the deafness. Only the brain and adjacent regions were extracted from each data set by manual masking. Individual PET image at normal and deaf state was realigned to each other to remove the confounding effects by the different spatial normalization parameters on the results of statistical analyses. Distance between the sampling points on the reference image and kernel size of Gaussian filter applied to the images before estimating the realignment parameters were adjusted to 0.5 mm and 2 mm. Both data was then spatial normalized onto study-specific cat brain template. Spatially normalized PET data were smoothed and voxel-based paired t-test was performed. Cerebral glucose metabolism decreased significantly after the loss of hearing capability in parietal lobes, postcentral gyri, STG, MTG, lTG, and IC at both hemisphere and left SC (FDR corrected P < 0.05, k=50). Cerebral glucose metabolism in deaf cats was found to be significantly higher than in controls in the right cingulate (FDR corrected P < 0.05, k=50). The ROI analysis also showed significant reduction of glucose metabolism in the same areas as in the SPM analysis, except for some regions (P < 0.05). Method for the voxel-based analysis of cat brain PET data was optimized for analysis of cat brain PET. This result was also confirmed by ROI analysis. The results obtained demonstrated the high localization accuracy and specificity of the developed method, and were found to be useful for examining cerebral glucose metabolism in a cat cortical deafness model.

Statistical trend analysis methods for temporal phenomena

Energy Technology Data Exchange (ETDEWEB)

Lehtinen, E.; Pulkkinen, U. [VTT Automation, (Finland); Poern, K. [Poern Consulting, Nykoeping (Sweden)

1997-04-01

We consider point events occurring in a random way in time. In many applications the pattern of occurrence is of intrinsic interest as indicating a trend or some other systematic feature in the rate of occurrence. The purpose of this report is to survey briefly different statistical trend analysis methods and illustrate their applicability to temporal phenomena in particular. The trend testing of point events is usually seen as the testing of the hypotheses concerning the intensity of the occurrence of events. When the intensity function is parametrized, the testing of trend is a typical parametric testing problem. In industrial applications the operational experience generally does not suggest any specified model and method in advance. Therefore, and particularly, if the Poisson process assumption is very questionable, it is desirable to apply tests that are valid for a wide variety of possible processes. The alternative approach for trend testing is to use some non-parametric procedure. In this report we have presented four non-parametric tests: The Cox-Stuart test, the Wilcoxon signed ranks test, the Mann test, and the exponential ordered scores test. In addition to the classical parametric and non-parametric approaches we have also considered the Bayesian trend analysis. First we discuss a Bayesian model, which is based on a power law intensity model. The Bayesian statistical inferences are based on the analysis of the posterior distribution of the trend parameters, and the probability of trend is immediately seen from these distributions. We applied some of the methods discussed in an example case. It should be noted, that this report is a feasibility study rather than a scientific evaluation of statistical methods, and the examples can only be seen as demonstrations of the methods. 14 refs, 10 figs.

Statistical trend analysis methods for temporal phenomena

International Nuclear Information System (INIS)

Lehtinen, E.; Pulkkinen, U.; Poern, K.

1997-04-01

We consider point events occurring in a random way in time. In many applications the pattern of occurrence is of intrinsic interest as indicating a trend or some other systematic feature in the rate of occurrence. The purpose of this report is to survey briefly different statistical trend analysis methods and illustrate their applicability to temporal phenomena in particular. The trend testing of point events is usually seen as the testing of the hypotheses concerning the intensity of the occurrence of events. When the intensity function is parametrized, the testing of trend is a typical parametric testing problem. In industrial applications the operational experience generally does not suggest any specified model and method in advance. Therefore, and particularly, if the Poisson process assumption is very questionable, it is desirable to apply tests that are valid for a wide variety of possible processes. The alternative approach for trend testing is to use some non-parametric procedure. In this report we have presented four non-parametric tests: The Cox-Stuart test, the Wilcoxon signed ranks test, the Mann test, and the exponential ordered scores test. In addition to the classical parametric and non-parametric approaches we have also considered the Bayesian trend analysis. First we discuss a Bayesian model, which is based on a power law intensity model. The Bayesian statistical inferences are based on the analysis of the posterior distribution of the trend parameters, and the probability of trend is immediately seen from these distributions. We applied some of the methods discussed in an example case. It should be noted, that this report is a feasibility study rather than a scientific evaluation of statistical methods, and the examples can only be seen as demonstrations of the methods

An Efficient Graph-based Method for Long-term Land-use Change Statistics

Directory of Open Access Journals (Sweden)

Yipeng Zhang

2015-12-01

Full Text Available Statistical analysis of land-use change plays an important role in sustainable land management and has received increasing attention from scholars and administrative departments. However, the statistical process involving spatial overlay analysis remains difficult and needs improvement to deal with mass land-use data. In this paper, we introduce a spatio-temporal flow network model to reveal the hidden relational information among spatio-temporal entities. Based on graph theory, the constant condition of saturated multi-commodity flow is derived. A new method based on a network partition technique of spatio-temporal flow network are proposed to optimize the transition statistical process. The effectiveness and efficiency of the proposed method is verified through experiments using land-use data in Hunan from 2009 to 2014. In the comparison among three different land-use change statistical methods, the proposed method exhibits remarkable superiority in efficiency.

Statistical wind analysis for near-space applications

Science.gov (United States)

Roney, Jason A.

2007-09-01

Statistical wind models were developed based on the existing observational wind data for near-space altitudes between 60 000 and 100 000 ft (18 30 km) above ground level (AGL) at two locations, Akon, OH, USA, and White Sands, NM, USA. These two sites are envisioned as playing a crucial role in the first flights of high-altitude airships. The analysis shown in this paper has not been previously applied to this region of the stratosphere for such an application. Standard statistics were compiled for these data such as mean, median, maximum wind speed, and standard deviation, and the data were modeled with Weibull distributions. These statistics indicated, on a yearly average, there is a lull or a “knee” in the wind between 65 000 and 72 000 ft AGL (20 22 km). From the standard statistics, trends at both locations indicated substantial seasonal variation in the mean wind speed at these heights. The yearly and monthly statistical modeling indicated that Weibull distributions were a reasonable model for the data. Forecasts and hindcasts were done by using a Weibull model based on 2004 data and comparing the model with the 2003 and 2005 data. The 2004 distribution was also a reasonable model for these years. Lastly, the Weibull distribution and cumulative function were used to predict the 50%, 95%, and 99% winds, which are directly related to the expected power requirements of a near-space station-keeping airship. These values indicated that using only the standard deviation of the mean may underestimate the operational conditions.

Development of statistical analysis code for meteorological data (W-View)

International Nuclear Information System (INIS)

Tachibana, Haruo; Sekita, Tsutomu; Yamaguchi, Takenori

2003-03-01

A computer code (W-View: Weather View) was developed to analyze the meteorological data statistically based on 'the guideline of meteorological statistics for the safety analysis of nuclear power reactor' (Nuclear Safety Commission on January 28, 1982; revised on March 29, 2001). The code gives statistical meteorological data to assess the public dose in case of normal operation and severe accident to get the license of nuclear reactor operation. This code was revised from the original code used in a large office computer code to enable a personal computer user to analyze the meteorological data simply and conveniently and to make the statistical data tables and figures of meteorology. (author)

Statistical margin to DNB safety analysis approach for LOFT

International Nuclear Information System (INIS)

Atkinson, S.A.

1982-01-01

A method was developed and used for LOFT thermal safety analysis to estimate the statistical margin to DNB for the hot rod, and to base safety analysis on desired DNB probability limits. This method is an advanced approach using response surface analysis methods, a very efficient experimental design, and a 2nd-order response surface equation with a 2nd-order error propagation analysis to define the MDNBR probability density function. Calculations for limiting transients were used in the response surface analysis thereby including transient interactions and trip uncertainties in the MDNBR probability density

Statistical considerations on safety analysis

International Nuclear Information System (INIS)

Pal, L.; Makai, M.

2004-01-01

The authors have investigated the statistical methods applied to safety analysis of nuclear reactors and arrived at alarming conclusions: a series of calculations with the generally appreciated safety code ATHLET were carried out to ascertain the stability of the results against input uncertainties in a simple experimental situation. Scrutinizing those calculations, we came to the conclusion that the ATHLET results may exhibit chaotic behavior. A further conclusion is that the technological limits are incorrectly set when the output variables are correlated. Another formerly unnoticed conclusion of the previous ATHLET calculations that certain innocent looking parameters (like wall roughness factor, the number of bubbles per unit volume, the number of droplets per unit volume) can influence considerably such output parameters as water levels. The authors are concerned with the statistical foundation of present day safety analysis practices and can only hope that their own misjudgment will be dispelled. Until then, the authors suggest applying correct statistical methods in safety analysis even if it makes the analysis more expensive. It would be desirable to continue exploring the role of internal parameters (wall roughness factor, steam-water surface in thermal hydraulics codes, homogenization methods in neutronics codes) in system safety codes and to study their effects on the analysis. In the validation and verification process of a code one carries out a series of computations. The input data are not precisely determined because measured data have an error, calculated data are often obtained from a more or less accurate model. Some users of large codes are content with comparing the nominal output obtained from the nominal input, whereas all the possible inputs should be taken into account when judging safety. At the same time, any statement concerning safety must be aleatory, and its merit can be judged only when the probability is known with which the

Statistical shape analysis with applications in R

CERN Document Server

Dryden, Ian L

2016-01-01

A thoroughly revised and updated edition of this introduction to modern statistical methods for shape analysis Shape analysis is an important tool in the many disciplines where objects are compared using geometrical features. Examples include comparing brain shape in schizophrenia; investigating protein molecules in bioinformatics; and describing growth of organisms in biology. This book is a significant update of the highly-regarded `Statistical Shape Analysis’ by the same authors. The new edition lays the foundations of landmark shape analysis, including geometrical concepts and statistical techniques, and extends to include analysis of curves, surfaces, images and other types of object data. Key definitions and concepts are discussed throughout, and the relative merits of different approaches are presented. The authors have included substantial new material on recent statistical developments and offer numerous examples throughout the text. Concepts are introduced in an accessible manner, while reta...

Quantification of Solar Cell Failure Signatures Based on Statistical Analysis of Electroluminescence Images

DEFF Research Database (Denmark)

Spataru, Sergiu; Parikh, Harsh; Hacke, Peter

2017-01-01

We demonstrate a method to quantify the extent of solar cell cracks, shunting, or damaged cell interconnects, present in crystalline silicon photovoltaic (PV) modules by statistical analysis of the electroluminescence (EL) intensity distributions of individual cells within the module. From the EL...... intensity distributions (ELID) of each cell, we calculated summary statistics such as standard deviation, median, skewness and kurtosis, and analyzed how they correlate with the magnitude of the solar cell degradation. We found that the dispersion of the ELID increases with the size and severity...

Damping layout optimization for ship's cabin noise reduction based on statistical energy analysis

Directory of Open Access Journals (Sweden)

WU Weiguo

2017-08-01

Full Text Available An optimization analysis study concerning the damping control of ship's cabin noise was carried out in order to improve the effect and reduce the weight of damping. Based on the Statistical Energy Analysis (SEA method, a theoretical deduction and numerical analysis of the first-order sensitivity analysis of the A-weighted sound pressure level concerning the damping loss factor of the subsystem were carried out. On this basis, a mathematical optimization model was proposed and an optimization program developed. Next, the secondary development of VA One software was implemented through the use of MATLAB, while the cabin noise damping control layout optimization system was established. Finally, the optimization model of the ship was constructed and numerical experiments of damping control optimization conducted. The damping installation region was divided into five parts with different damping thicknesses. The total weight of damping was set as an objective function and the A-weighted sound pressure level of the target cabin was set as a constraint condition. The best damping thickness was obtained through the optimization program, and the total damping weight was reduced by 60.4%. The results show that the damping noise reduction effect of unit weight is significantly improved through the optimization method. This research successfully solves the installation position and thickness selection problems in the acoustic design of damping control, providing a reliable analysis method and guidance for the design.

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

Science.gov (United States)

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

2018-01-01

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

Hybrid statistics-simulations based method for atom-counting from ADF STEM images.

Science.gov (United States)

De Wael, Annelies; De Backer, Annick; Jones, Lewys; Nellist, Peter D; Van Aert, Sandra

2017-06-01

A hybrid statistics-simulations based method for atom-counting from annular dark field scanning transmission electron microscopy (ADF STEM) images of monotype crystalline nanostructures is presented. Different atom-counting methods already exist for model-like systems. However, the increasing relevance of radiation damage in the study of nanostructures demands a method that allows atom-counting from low dose images with a low signal-to-noise ratio. Therefore, the hybrid method directly includes prior knowledge from image simulations into the existing statistics-based method for atom-counting, and accounts in this manner for possible discrepancies between actual and simulated experimental conditions. It is shown by means of simulations and experiments that this hybrid method outperforms the statistics-based method, especially for low electron doses and small nanoparticles. The analysis of a simulated low dose image of a small nanoparticle suggests that this method allows for far more reliable quantitative analysis of beam-sensitive materials. Copyright © 2017 Elsevier B.V. All rights reserved.

Fisher statistics for analysis of diffusion tensor directional information.

Science.gov (United States)

Hutchinson, Elizabeth B; Rutecki, Paul A; Alexander, Andrew L; Sutula, Thomas P

2012-04-30

A statistical approach is presented for the quantitative analysis of diffusion tensor imaging (DTI) directional information using Fisher statistics, which were originally developed for the analysis of vectors in the field of paleomagnetism. In this framework, descriptive and inferential statistics have been formulated based on the Fisher probability density function, a spherical analogue of the normal distribution. The Fisher approach was evaluated for investigation of rat brain DTI maps to characterize tissue orientation in the corpus callosum, fornix, and hilus of the dorsal hippocampal dentate gyrus, and to compare directional properties in these regions following status epilepticus (SE) or traumatic brain injury (TBI) with values in healthy brains. Direction vectors were determined for each region of interest (ROI) for each brain sample and Fisher statistics were applied to calculate the mean direction vector and variance parameters in the corpus callosum, fornix, and dentate gyrus of normal rats and rats that experienced TBI or SE. Hypothesis testing was performed by calculation of Watson's F-statistic and associated p-value giving the likelihood that grouped observations were from the same directional distribution. In the fornix and midline corpus callosum, no directional differences were detected between groups, however in the hilus, significant (pstatistical comparison of tissue structural orientation. Copyright © 2012 Elsevier B.V. All rights reserved.

Quantification of solar cell failure signatures based on statistical analysis of electroluminescence images

DEFF Research Database (Denmark)

Spataru, Sergiu; Parikh, Harsh; Benatto, Gisele Alves dos Reis

2017-01-01

We propose a method to identify and quantify the extent of solar cell cracks, shunting, or damaged cell interconnects, present in crystalline silicon photovoltaic (PV) modules by statistical analysis of the electroluminescence (EL) intensity distributions of individual cells within the module. From...... the EL intensity distributions (ELID) of each cell, we calculated summary statistics such as standard deviation, median, skewness and kurtosis, and analyzed how they correlate with the type of the solar cell degradation. We found that the dispersion of the ELID increases with the size and severity...

Application of descriptive statistics in analysis of experimental data

OpenAIRE

Mirilović Milorad; Pejin Ivana

2008-01-01

Statistics today represent a group of scientific methods for the quantitative and qualitative investigation of variations in mass appearances. In fact, statistics present a group of methods that are used for the accumulation, analysis, presentation and interpretation of data necessary for reaching certain conclusions. Statistical analysis is divided into descriptive statistical analysis and inferential statistics. The values which represent the results of an experiment, and which are the subj...

Hybrid statistics-simulations based method for atom-counting from ADF STEM images

Energy Technology Data Exchange (ETDEWEB)

De wael, Annelies, E-mail: annelies.dewael@uantwerpen.be [Electron Microscopy for Materials Science (EMAT), University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp (Belgium); De Backer, Annick [Electron Microscopy for Materials Science (EMAT), University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp (Belgium); Jones, Lewys; Nellist, Peter D. [Department of Materials, University of Oxford, Parks Road, OX1 3PH Oxford (United Kingdom); Van Aert, Sandra, E-mail: sandra.vanaert@uantwerpen.be [Electron Microscopy for Materials Science (EMAT), University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp (Belgium)

2017-06-15

A hybrid statistics-simulations based method for atom-counting from annular dark field scanning transmission electron microscopy (ADF STEM) images of monotype crystalline nanostructures is presented. Different atom-counting methods already exist for model-like systems. However, the increasing relevance of radiation damage in the study of nanostructures demands a method that allows atom-counting from low dose images with a low signal-to-noise ratio. Therefore, the hybrid method directly includes prior knowledge from image simulations into the existing statistics-based method for atom-counting, and accounts in this manner for possible discrepancies between actual and simulated experimental conditions. It is shown by means of simulations and experiments that this hybrid method outperforms the statistics-based method, especially for low electron doses and small nanoparticles. The analysis of a simulated low dose image of a small nanoparticle suggests that this method allows for far more reliable quantitative analysis of beam-sensitive materials. - Highlights: • A hybrid method for atom-counting from ADF STEM images is introduced. • Image simulations are incorporated into a statistical framework in a reliable manner. • Limits of the existing methods for atom-counting are far exceeded. • Reliable counting results from an experimental low dose image are obtained. • Progress towards reliable quantitative analysis of beam-sensitive materials is made.

Statistical Analysis of Research Data | Center for Cancer Research

Science.gov (United States)

Recent advances in cancer biology have resulted in the need for increased statistical analysis of research data. The Statistical Analysis of Research Data (SARD) course will be held on April 5-6, 2018 from 9 a.m.-5 p.m. at the National Institutes of Health's Natcher Conference Center, Balcony C on the Bethesda Campus. SARD is designed to provide an overview on the general principles of statistical analysis of research data.  The first day will feature univariate data analysis, including descriptive statistics, probability distributions, one- and two-sample inferential statistics.

Development of statistical analysis code for meteorological data (W-View)

Energy Technology Data Exchange (ETDEWEB)

Tachibana, Haruo; Sekita, Tsutomu; Yamaguchi, Takenori [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

2003-03-01

A computer code (W-View: Weather View) was developed to analyze the meteorological data statistically based on 'the guideline of meteorological statistics for the safety analysis of nuclear power reactor' (Nuclear Safety Commission on January 28, 1982; revised on March 29, 2001). The code gives statistical meteorological data to assess the public dose in case of normal operation and severe accident to get the license of nuclear reactor operation. This code was revised from the original code used in a large office computer code to enable a personal computer user to analyze the meteorological data simply and conveniently and to make the statistical data tables and figures of meteorology. (author)

Statistical analysis of tourism destination competitiveness

Directory of Open Access Journals (Sweden)

Attilio Gardini

2013-05-01

Full Text Available The growing relevance of tourism industry for modern advanced economies has increased the interest among researchers and policy makers in the statistical analysis of destination competitiveness. In this paper we outline a new model of destination competitiveness based on sound theoretical grounds and we develop a statistical test of the model on sample data based on Italian tourist destination decisions and choices. Our model focuses on the tourism decision process which starts from the demand schedule for holidays and ends with the choice of a specific holiday destination. The demand schedule is a function of individual preferences and of destination positioning, while the final decision is a function of the initial demand schedule and the information concerning services for accommodation and recreation in the selected destinations. Moreover, we extend previous studies that focused on image or attributes (such as climate and scenery by paying more attention to the services for accommodation and recreation in the holiday destinations. We test the proposed model using empirical data collected from a sample of 1.200 Italian tourists interviewed in 2007 (October - December. Data analysis shows that the selection probability for the destination included in the consideration set is not proportional to the share of inclusion because the share of inclusion is determined by the brand image, while the selection of the effective holiday destination is influenced by the real supply conditions. The analysis of Italian tourists preferences underline the existence of a latent demand for foreign holidays which points out a risk of market share reduction for Italian tourism system in the global market. We also find a snow ball effect which helps the most popular destinations, mainly in the northern Italian regions.

Statistical analysis with Excel for dummies

CERN Document Server

Schmuller, Joseph

2013-01-01

Take the mystery out of statistical terms and put Excel to work! If you need to create and interpret statistics in business or classroom settings, this easy-to-use guide is just what you need. It shows you how to use Excel's powerful tools for statistical analysis, even if you've never taken a course in statistics. Learn the meaning of terms like mean and median, margin of error, standard deviation, and permutations, and discover how to interpret the statistics of everyday life. You'll learn to use Excel formulas, charts, PivotTables, and other tools to make sense of everything fro

Statistical Image Analysis of Tomograms with Application to Fibre Geometry Characterisation

DEFF Research Database (Denmark)

Emerson, Monica Jane

The goal of this thesis is to develop statistical image analysis tools to characterise the micro-structure of complex materials used in energy technologies, with a strong focus on fibre composites. These quantification tools are based on extracting geometrical parameters defining structures from 2D...... with high resolution both in space and time to observe fast micro-structural changes. This thesis demonstrates that statistical image analysis combined with X-ray CT opens up numerous possibilities for understanding the behaviour of fibre composites under real life conditions. Besides enabling...

Radar Derived Spatial Statistics of Summer Rain. Volume 2; Data Reduction and Analysis

Science.gov (United States)

Konrad, T. G.; Kropfli, R. A.

1975-01-01

Data reduction and analysis procedures are discussed along with the physical and statistical descriptors used. The statistical modeling techniques are outlined and examples of the derived statistical characterization of rain cells in terms of the several physical descriptors are presented. Recommendations concerning analyses which can be pursued using the data base collected during the experiment are included.

Statistical analysis of dynamic parameters of the core

International Nuclear Information System (INIS)

Ionov, V.S.

2007-01-01

The transients of various types were investigated for the cores of zero power critical facilities in RRC KI and NPP. Dynamic parameters of neutron transients were explored by tool statistical analysis. Its have sufficient duration, few channels for currents of chambers and reactivity and also some channels for technological parameters. On these values the inverse period. reactivity, lifetime of neutrons, reactivity coefficients and some effects of a reactivity are determinate, and on the values were restored values of measured dynamic parameters as result of the analysis. The mathematical means of statistical analysis were used: approximation(A), filtration (F), rejection (R), estimation of parameters of descriptive statistic (DSP), correlation performances (kk), regression analysis(KP), the prognosis (P), statistician criteria (SC). The calculation procedures were realized by computer language MATLAB. The reasons of methodical and statistical errors are submitted: inadequacy of model operation, precision neutron-physical parameters, features of registered processes, used mathematical model in reactivity meters, technique of processing for registered data etc. Examples of results of statistical analysis. Problems of validity of the methods used for definition and certification of values of statistical parameters and dynamic characteristics are considered (Authors)

Recent advances in statistical energy analysis

Science.gov (United States)

Heron, K. H.

1992-01-01

Statistical Energy Analysis (SEA) has traditionally been developed using modal summation and averaging approach, and has led to the need for many restrictive SEA assumptions. The assumption of 'weak coupling' is particularly unacceptable when attempts are made to apply SEA to structural coupling. It is now believed that this assumption is more a function of the modal formulation rather than a necessary formulation of SEA. The present analysis ignores this restriction and describes a wave approach to the calculation of plate-plate coupling loss factors. Predictions based on this method are compared with results obtained from experiments using point excitation on one side of an irregular six-sided box structure. Conclusions show that the use and calculation of infinite transmission coefficients is the way forward for the development of a purely predictive SEA code.

General specifications for the development of a USL NASA PC R and D statistical analysis support package

Science.gov (United States)

Dominick, Wayne D. (Editor); Bassari, Jinous; Triantafyllopoulos, Spiros

1984-01-01

The University of Southwestern Louisiana (USL) NASA PC R and D statistical analysis support package is designed to be a three-level package to allow statistical analysis for a variety of applications within the USL Data Base Management System (DBMS) contract work. The design addresses usage of the statistical facilities as a library package, as an interactive statistical analysis system, and as a batch processing package.

CONFIDENCE LEVELS AND/VS. STATISTICAL HYPOTHESIS TESTING IN STATISTICAL ANALYSIS. CASE STUDY

Directory of Open Access Journals (Sweden)

ILEANA BRUDIU

2009-05-01

Full Text Available Estimated parameters with confidence intervals and testing statistical assumptions used in statistical analysis to obtain conclusions on research from a sample extracted from the population. Paper to the case study presented aims to highlight the importance of volume of sample taken in the study and how this reflects on the results obtained when using confidence intervals and testing for pregnant. If statistical testing hypotheses not only give an answer "yes" or "no" to some questions of statistical estimation using statistical confidence intervals provides more information than a test statistic, show high degree of uncertainty arising from small samples and findings build in the "marginally significant" or "almost significant (p very close to 0.05.

A powerful score-based test statistic for detecting gene-gene co-association.

Science.gov (United States)

Xu, Jing; Yuan, Zhongshang; Ji, Jiadong; Zhang, Xiaoshuai; Li, Hongkai; Wu, Xuesen; Xue, Fuzhong; Liu, Yanxun

2016-01-29

The genetic variants identified by Genome-wide association study (GWAS) can only account for a small proportion of the total heritability for complex disease. The existence of gene-gene joint effects which contains the main effects and their co-association is one of the possible explanations for the "missing heritability" problems. Gene-gene co-association refers to the extent to which the joint effects of two genes differ from the main effects, not only due to the traditional interaction under nearly independent condition but the correlation between genes. Generally, genes tend to work collaboratively within specific pathway or network contributing to the disease and the specific disease-associated locus will often be highly correlated (e.g. single nucleotide polymorphisms (SNPs) in linkage disequilibrium). Therefore, we proposed a novel score-based statistic (SBS) as a gene-based method for detecting gene-gene co-association. Various simulations illustrate that, under different sample sizes, marginal effects of causal SNPs and co-association levels, the proposed SBS has the better performance than other existed methods including single SNP-based and principle component analysis (PCA)-based logistic regression model, the statistics based on canonical correlations (CCU), kernel canonical correlation analysis (KCCU), partial least squares path modeling (PLSPM) and delta-square (δ (2)) statistic. The real data analysis of rheumatoid arthritis (RA) further confirmed its advantages in practice. SBS is a powerful and efficient gene-based method for detecting gene-gene co-association.

Collecting operational event data for statistical analysis

International Nuclear Information System (INIS)

Atwood, C.L.

1994-09-01

This report gives guidance for collecting operational data to be used for statistical analysis, especially analysis of event counts. It discusses how to define the purpose of the study, the unit (system, component, etc.) to be studied, events to be counted, and demand or exposure time. Examples are given of classification systems for events in the data sources. A checklist summarizes the essential steps in data collection for statistical analysis

A Statistical Primer: Understanding Descriptive and Inferential Statistics

OpenAIRE

Gillian Byrne

2007-01-01

As libraries and librarians move more towards evidence‐based decision making, the data being generated in libraries is growing. Understanding the basics of statistical analysis is crucial for evidence‐based practice (EBP), in order to correctly design and analyze researchas well as to evaluate the research of others. This article covers the fundamentals of descriptive and inferential statistics, from hypothesis construction to sampling to common statistical techniques including chi‐square, co...

Advanced data analysis in neuroscience integrating statistical and computational models

CERN Document Server

Durstewitz, Daniel

2017-01-01

This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering.  Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanat ory frameworks, but become powerfu...

Statistics and analysis of scientific data

CERN Document Server

Bonamente, Massimiliano

2013-01-01

Statistics and Analysis of Scientific Data covers the foundations of probability theory and statistics, and a number of numerical and analytical methods that are essential for the present-day analyst of scientific data. Topics covered include probability theory, distribution functions of statistics, fits to two-dimensional datasheets and parameter estimation, Monte Carlo methods and Markov chains. Equal attention is paid to the theory and its practical application, and results from classic experiments in various fields are used to illustrate the importance of statistics in the analysis of scientific data. The main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and proactive use of the material for practical applications. The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher. Basic calculus is us...

GIS-based bivariate statistical techniques for groundwater potential analysis (an example of Iran)

Science.gov (United States)

Haghizadeh, Ali; Moghaddam, Davoud Davoudi; Pourghasemi, Hamid Reza

2017-12-01

Groundwater potential analysis prepares better comprehension of hydrological settings of different regions. This study shows the potency of two GIS-based data driven bivariate techniques namely statistical index (SI) and Dempster-Shafer theory (DST) to analyze groundwater potential in Broujerd region of Iran. The research was done using 11 groundwater conditioning factors and 496 spring positions. Based on the ground water potential maps (GPMs) of SI and DST methods, 24.22% and 23.74% of the study area is covered by poor zone of groundwater potential, and 43.93% and 36.3% of Broujerd region is covered by good and very good potential zones, respectively. The validation of outcomes displayed that area under the curve (AUC) of SI and DST techniques are 81.23% and 79.41%, respectively, which shows SI method has slightly a better performance than the DST technique. Therefore, SI and DST methods are advantageous to analyze groundwater capacity and scrutinize the complicated relation between groundwater occurrence and groundwater conditioning factors, which permits investigation of both systemic and stochastic uncertainty. Finally, it can be realized that these techniques are very beneficial for groundwater potential analyzing and can be practical for water-resource management experts.

Advances in statistical models for data analysis

CERN Document Server

Minerva, Tommaso; Vichi, Maurizio

2015-01-01

This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas such as economics, marketing, education, social sciences and environment. The papers in this volume were first presented at the 9th biannual meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in September 2013 at the University of Modena and Reggio Emilia, Italy.

An Application of Multivariate Statistical Analysis for Query-Driven Visualization

Energy Technology Data Exchange (ETDEWEB)

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

2011-03-01

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

Statistical Analysis of Radio Propagation Channel in Ruins Environment

Directory of Open Access Journals (Sweden)

Jiao He

2015-01-01

Full Text Available The cellphone based localization system for search and rescue in complex high density ruins has attracted a great interest in recent years, where the radio channel characteristics are critical for design and development of such a system. This paper presents a spatial smoothing estimation via rotational invariance technique (SS-ESPRIT for radio channel characterization of high density ruins. The radio propagations at three typical mobile communication bands (0.9, 1.8, and 2 GHz are investigated in two different scenarios. Channel parameters, such as arrival time, delays, and complex amplitudes, are statistically analyzed. Furthermore, a channel simulator is built based on these statistics. By comparison analysis of average excess delay and delay spread, the validation results show a good agreement between the measurements and channel modeling results.

Analysis of statistical misconception in terms of statistical reasoning

Science.gov (United States)

Maryati, I.; Priatna, N.

2018-05-01

Reasoning skill is needed for everyone to face globalization era, because every person have to be able to manage and use information from all over the world which can be obtained easily. Statistical reasoning skill is the ability to collect, group, process, interpret, and draw conclusion of information. Developing this skill can be done through various levels of education. However, the skill is low because many people assume that statistics is just the ability to count and using formulas and so do students. Students still have negative attitude toward course which is related to research. The purpose of this research is analyzing students’ misconception in descriptive statistic course toward the statistical reasoning skill. The observation was done by analyzing the misconception test result and statistical reasoning skill test; observing the students’ misconception effect toward statistical reasoning skill. The sample of this research was 32 students of math education department who had taken descriptive statistic course. The mean value of misconception test was 49,7 and standard deviation was 10,6 whereas the mean value of statistical reasoning skill test was 51,8 and standard deviation was 8,5. If the minimal value is 65 to state the standard achievement of a course competence, students’ mean value is lower than the standard competence. The result of students’ misconception study emphasized on which sub discussion that should be considered. Based on the assessment result, it was found that students’ misconception happen on this: 1) writing mathematical sentence and symbol well, 2) understanding basic definitions, 3) determining concept that will be used in solving problem. In statistical reasoning skill, the assessment was done to measure reasoning from: 1) data, 2) representation, 3) statistic format, 4) probability, 5) sample, and 6) association.

Cloud-based solution to identify statistically significant MS peaks differentiating sample categories.

Science.gov (United States)

Ji, Jun; Ling, Jeffrey; Jiang, Helen; Wen, Qiaojun; Whitin, John C; Tian, Lu; Cohen, Harvey J; Ling, Xuefeng B

2013-03-23

Mass spectrometry (MS) has evolved to become the primary high throughput tool for proteomics based biomarker discovery. Until now, multiple challenges in protein MS data analysis remain: large-scale and complex data set management; MS peak identification, indexing; and high dimensional peak differential analysis with the concurrent statistical tests based false discovery rate (FDR). "Turnkey" solutions are needed for biomarker investigations to rapidly process MS data sets to identify statistically significant peaks for subsequent validation. Here we present an efficient and effective solution, which provides experimental biologists easy access to "cloud" computing capabilities to analyze MS data. The web portal can be accessed at http://transmed.stanford.edu/ssa/. Presented web application supplies large scale MS data online uploading and analysis with a simple user interface. This bioinformatic tool will facilitate the discovery of the potential protein biomarkers using MS.

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

Science.gov (United States)

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

2017-03-01

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

Reconstructing Macroeconomics Based on Statistical Physics

Science.gov (United States)

Aoki, Masanao; Yoshikawa, Hiroshi

We believe that time has come to integrate the new approach based on statistical physics or econophysics into macroeconomics. Toward this goal, there must be more dialogues between physicists and economists. In this paper, we argue that there is no reason why the methods of statistical physics so successful in many fields of natural sciences cannot be usefully applied to macroeconomics that is meant to analyze the macroeconomy comprising a large number of economic agents. It is, in fact, weird to regard the macroeconomy as a homothetic enlargement of the representative micro agent. We trust the bright future of the new approach to macroeconomies based on statistical physics.

The Practicality of Statistical Physics Handout Based on KKNI and the Constructivist Approach

Science.gov (United States)

Sari, S. Y.; Afrizon, R.

2018-04-01

Statistical physics lecture shows that: 1) the performance of lecturers, social climate, students’ competence and soft skills needed at work are in enough category, 2) students feel difficulties in following the lectures of statistical physics because it is abstract, 3) 40.72% of students needs more understanding in the form of repetition, practice questions and structured tasks, and 4) the depth of statistical physics material needs to be improved gradually and structured. This indicates that learning materials in accordance of The Indonesian National Qualification Framework or Kerangka Kualifikasi Nasional Indonesia (KKNI) with the appropriate learning approach are needed to help lecturers and students in lectures. The author has designed statistical physics handouts which have very valid criteria (90.89%) according to expert judgment. In addition, the practical level of handouts designed also needs to be considered in order to be easy to use, interesting and efficient in lectures. The purpose of this research is to know the practical level of statistical physics handout based on KKNI and a constructivist approach. This research is a part of research and development with 4-D model developed by Thiagarajan. This research activity has reached part of development test at Development stage. Data collection took place by using a questionnaire distributed to lecturers and students. Data analysis using descriptive data analysis techniques in the form of percentage. The analysis of the questionnaire shows that the handout of statistical physics has very practical criteria. The conclusion of this study is statistical physics handouts based on the KKNI and constructivist approach have been practically used in lectures.

Statistical models and methods for reliability and survival analysis

CERN Document Server

Couallier, Vincent; Huber-Carol, Catherine; Mesbah, Mounir; Huber -Carol, Catherine; Limnios, Nikolaos; Gerville-Reache, Leo

2013-01-01

Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical

Classification, (big) data analysis and statistical learning

CERN Document Server

Conversano, Claudio; Vichi, Maurizio

2018-01-01

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas such as economics, marketing, education, social sciences, medicine, environmental sciences and the pharmaceutical industry. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field. The peer-reviewed contributions were presented at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in Santa Margherita di Pul...

Statistical hot spot analysis of reactor cores

International Nuclear Information System (INIS)

Schaefer, H.

1974-05-01

This report is an introduction into statistical hot spot analysis. After the definition of the term 'hot spot' a statistical analysis is outlined. The mathematical method is presented, especially the formula concerning the probability of no hot spots in a reactor core is evaluated. A discussion with the boundary conditions of a statistical hot spot analysis is given (technological limits, nominal situation, uncertainties). The application of the hot spot analysis to the linear power of pellets and the temperature rise in cooling channels is demonstrated with respect to the test zone of KNK II. Basic values, such as probability of no hot spots, hot spot potential, expected hot spot diagram and cumulative distribution function of hot spots, are discussed. It is shown, that the risk of hot channels can be dispersed equally over all subassemblies by an adequate choice of the nominal temperature distribution in the core

The statistical analysis of anisotropies

International Nuclear Information System (INIS)

Webster, A.

1977-01-01

One of the many uses to which a radio survey may be put is an analysis of the distribution of the radio sources on the celestial sphere to find out whether they are bunched into clusters or lie in preferred regions of space. There are many methods of testing for clustering in point processes and since they are not all equally good this contribution is presented as a brief guide to what seems to be the best of them. The radio sources certainly do not show very strong clusering and may well be entirely unclustered so if a statistical method is to be useful it must be both powerful and flexible. A statistic is powerful in this context if it can efficiently distinguish a weakly clustered distribution of sources from an unclustered one, and it is flexible if it can be applied in a way which avoids mistaking defects in the survey for true peculiarities in the distribution of sources. The paper divides clustering statistics into two classes: number density statistics and log N/log S statistics. (Auth.)

A Matlab user interface for the statistically assisted fluid registration algorithm and tensor-based morphometry

Science.gov (United States)

Yepes-Calderon, Fernando; Brun, Caroline; Sant, Nishita; Thompson, Paul; Lepore, Natasha

2015-01-01

Tensor-Based Morphometry (TBM) is an increasingly popular method for group analysis of brain MRI data. The main steps in the analysis consist of a nonlinear registration to align each individual scan to a common space, and a subsequent statistical analysis to determine morphometric differences, or difference in fiber structure between groups. Recently, we implemented the Statistically-Assisted Fluid Registration Algorithm or SAFIRA,1 which is designed for tracking morphometric differences among populations. To this end, SAFIRA allows the inclusion of statistical priors extracted from the populations being studied as regularizers in the registration. This flexibility and degree of sophistication limit the tool to expert use, even more so considering that SAFIRA was initially implemented in command line mode. Here, we introduce a new, intuitive, easy to use, Matlab-based graphical user interface for SAFIRA's multivariate TBM. The interface also generates different choices for the TBM statistics, including both the traditional univariate statistics on the Jacobian matrix, and comparison of the full deformation tensors.2 This software will be freely disseminated to the neuroimaging research community.

Basic statistical tools in research and data analysis

Directory of Open Access Journals (Sweden)

Zulfiqar Ali

2016-01-01

Full Text Available Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

Computer-Based Learning: The Use of SPSS Statistical Program for Improving Biostatistical Competence of Medical Students

Directory of Open Access Journals (Sweden)

Zvi H. Perry

2014-01-01

Full Text Available Background. We changed the biostatistics curriculum for our medical students and have created a course entitled “Multivariate analysis of statistical data, using the SPSS package.” Purposes. The aim of this course was to develop students’ skills in computerized data analysis, as well as enhancing their ability to read and interpret statistical data analysis in the literature. Methods. In the current study we have shown that a computer-based course for biostatistics and advanced data analysis is feasible and efficient, using course specific evaluation questionnaires. Results. Its efficacy is both subjective (our subjects felt better prepared to do their theses, as well as to read articles with advanced statistical data analysis and objective (their knowledge of how and when to apply statistical procedures seemed to improve. Conclusions. We showed that a formal evaluative process for such a course is possible and that it enhances the learning experience both for the students and their teachers. In the current study we have shown that a computer-based course for biostatistics and advanced data analysis is feasible and efficient.

Bayesian statistics applied to neutron activation data for reactor flux spectrum analysis

International Nuclear Information System (INIS)

Chiesa, Davide; Previtali, Ezio; Sisti, Monica

2014-01-01

Highlights: • Bayesian statistics to analyze the neutron flux spectrum from activation data. • Rigorous statistical approach for accurate evaluation of the neutron flux groups. • Cross section and activation data uncertainties included for the problem solution. • Flexible methodology applied to analyze different nuclear reactor flux spectra. • The results are in good agreement with the MCNP simulations of neutron fluxes. - Abstract: In this paper, we present a statistical method, based on Bayesian statistics, to analyze the neutron flux spectrum from the activation data of different isotopes. The experimental data were acquired during a neutron activation experiment performed at the TRIGA Mark II reactor of Pavia University (Italy) in four irradiation positions characterized by different neutron spectra. In order to evaluate the neutron flux spectrum, subdivided in energy groups, a system of linear equations, containing the group effective cross sections and the activation rate data, has to be solved. However, since the system’s coefficients are experimental data affected by uncertainties, a rigorous statistical approach is fundamental for an accurate evaluation of the neutron flux groups. For this purpose, we applied the Bayesian statistical analysis, that allows to include the uncertainties of the coefficients and the a priori information about the neutron flux. A program for the analysis of Bayesian hierarchical models, based on Markov Chain Monte Carlo (MCMC) simulations, was used to define the problem statistical model and solve it. The first analysis involved the determination of the thermal, resonance-intermediate and fast flux components and the dependence of the results on the Prior distribution choice was investigated to confirm the reliability of the Bayesian analysis. After that, the main resonances of the activation cross sections were analyzed to implement multi-group models with finer energy subdivisions that would allow to determine the

Statistical analysis and data management

International Nuclear Information System (INIS)

Anon.

1981-01-01

This report provides an overview of the history of the WIPP Biology Program. The recommendations of the American Institute of Biological Sciences (AIBS) for the WIPP biology program are summarized. The data sets available for statistical analyses and problems associated with these data sets are also summarized. Biological studies base maps are presented. A statistical model is presented to evaluate any correlation between climatological data and small mammal captures. No statistically significant relationship between variance in small mammal captures on Dr. Gennaro's 90m x 90m grid and precipitation records from the Duval Potash Mine were found

Reproducible statistical analysis with multiple languages

DEFF Research Database (Denmark)

Lenth, Russell; Højsgaard, Søren

2011-01-01

This paper describes the system for making reproducible statistical analyses. differs from other systems for reproducible analysis in several ways. The two main differences are: (1) Several statistics programs can be in used in the same document. (2) Documents can be prepared using OpenOffice or ......Office or \\LaTeX. The main part of this paper is an example showing how to use and together in an OpenOffice text document. The paper also contains some practical considerations on the use of literate programming in statistics....

A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data.

Science.gov (United States)

Lai, En-Yu; Chen, Yi-Hau; Wu, Kun-Pin

2017-06-01

Approaches to identify significant pathways from high-throughput quantitative data have been developed in recent years. Still, the analysis of proteomic data stays difficult because of limited sample size. This limitation also leads to the practice of using a competitive null as common approach; which fundamentally implies genes or proteins as independent units. The independent assumption ignores the associations among biomolecules with similar functions or cellular localization, as well as the interactions among them manifested as changes in expression ratios. Consequently, these methods often underestimate the associations among biomolecules and cause false positives in practice. Some studies incorporate the sample covariance matrix into the calculation to address this issue. However, sample covariance may not be a precise estimation if the sample size is very limited, which is usually the case for the data produced by mass spectrometry. In this study, we introduce a multivariate test under a self-contained null to perform pathway analysis for quantitative proteomic data. The covariance matrix used in the test statistic is constructed by the confidence scores retrieved from the STRING database or the HitPredict database. We also design an integrating procedure to retain pathways of sufficient evidence as a pathway group. The performance of the proposed T2-statistic is demonstrated using five published experimental datasets: the T-cell activation, the cAMP/PKA signaling, the myoblast differentiation, and the effect of dasatinib on the BCR-ABL pathway are proteomic datasets produced by mass spectrometry; and the protective effect of myocilin via the MAPK signaling pathway is a gene expression dataset of limited sample size. Compared with other popular statistics, the proposed T2-statistic yields more accurate descriptions in agreement with the discussion of the original publication. We implemented the T2-statistic into an R package T2GA, which is available at https

On two methods of statistical image analysis

NARCIS (Netherlands)

Missimer, J; Knorr, U; Maguire, RP; Herzog, H; Seitz, RJ; Tellman, L; Leenders, K.L.

1999-01-01

The computerized brain atlas (CBA) and statistical parametric mapping (SPM) are two procedures for voxel-based statistical evaluation of PET activation studies. Each includes spatial standardization of image volumes, computation of a statistic, and evaluation of its significance. In addition,

Rule-based statistical data mining agents for an e-commerce application

Science.gov (United States)

Qin, Yi; Zhang, Yan-Qing; King, K. N.; Sunderraman, Rajshekhar

2003-03-01

Intelligent data mining techniques have useful e-Business applications. Because an e-Commerce application is related to multiple domains such as statistical analysis, market competition, price comparison, profit improvement and personal preferences, this paper presents a hybrid knowledge-based e-Commerce system fusing intelligent techniques, statistical data mining, and personal information to enhance QoS (Quality of Service) of e-Commerce. A Web-based e-Commerce application software system, eDVD Web Shopping Center, is successfully implemented uisng Java servlets and an Oracle81 database server. Simulation results have shown that the hybrid intelligent e-Commerce system is able to make smart decisions for different customers.

Common pitfalls in statistical analysis: "P" values, statistical significance and confidence intervals

Directory of Open Access Journals (Sweden)

Priya Ranganathan

2015-01-01

Full Text Available In the second part of a series on pitfalls in statistical analysis, we look at various ways in which a statistically significant study result can be expressed. We debunk some of the myths regarding the ′P′ value, explain the importance of ′confidence intervals′ and clarify the importance of including both values in a paper

Using R and RStudio for data management, statistical analysis and graphics

CERN Document Server

Horton, Nicholas J

2015-01-01

This is the second edition of the popular book on using R for statistical analysis and graphics. The authors, who run a popular blog supplementing their books, have focused on adding many new examples to this new edition. These examples are presented primarily in new chapters based on the following themes: simulation, probability, statistics, mathematics/computing, and graphics. The authors have also added many other updates, including a discussion of RStudio-a very popular development environment for R.

Software for statistical data analysis used in Higgs searches

International Nuclear Information System (INIS)

Gumpert, Christian; Moneta, Lorenzo; Cranmer, Kyle; Kreiss, Sven; Verkerke, Wouter

2014-01-01

The analysis and interpretation of data collected by the Large Hadron Collider (LHC) requires advanced statistical tools in order to quantify the agreement between observation and theoretical models. RooStats is a project providing a statistical framework for data analysis with the focus on discoveries, confidence intervals and combination of different measurements in both Bayesian and frequentist approaches. It employs the RooFit data modelling language where mathematical concepts such as variables, (probability density) functions and integrals are represented as C++ objects. RooStats and RooFit rely on the persistency technology of the ROOT framework. The usage of a common data format enables the concept of digital publishing of complicated likelihood functions. The statistical tools have been developed in close collaboration with the LHC experiments to ensure their applicability to real-life use cases. Numerous physics results have been produced using the RooStats tools, with the discovery of the Higgs boson by the ATLAS and CMS experiments being certainly the most popular among them. We will discuss tools currently used by LHC experiments to set exclusion limits, to derive confidence intervals and to estimate discovery significances based on frequentist statistics and the asymptotic behaviour of likelihood functions. Furthermore, new developments in RooStats and performance optimisation necessary to cope with complex models depending on more than 1000 variables will be reviewed

Statistics and analysis of scientific data

CERN Document Server

Bonamente, Massimiliano

2017-01-01

The revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. It covers a broad range of numerical and analytical methods that are essential for the correct analysis of scientific data, including probability theory, distribution functions of statistics, fits to two-dimensional data and parameter estimation, Monte Carlo methods and Markov chains. Features new to this edition include: • a discussion of statistical techniques employed in business science, such as multiple regression analysis of multivariate datasets. • a new chapter on the various measures of the mean including logarithmic averages. • new chapters on systematic errors and intrinsic scatter, and on the fitting of data with bivariate errors. • a new case study and additional worked examples. • mathematical derivations and theoretical background material have been appropriately marked,to improve the readabili...

Benchmark validation of statistical models: Application to mediation analysis of imagery and memory.

Science.gov (United States)

MacKinnon, David P; Valente, Matthew J; Wurpts, Ingrid C

2018-03-29

This article describes benchmark validation, an approach to validating a statistical model. According to benchmark validation, a valid model generates estimates and research conclusions consistent with a known substantive effect. Three types of benchmark validation-(a) benchmark value, (b) benchmark estimate, and (c) benchmark effect-are described and illustrated with examples. Benchmark validation methods are especially useful for statistical models with assumptions that are untestable or very difficult to test. Benchmark effect validation methods were applied to evaluate statistical mediation analysis in eight studies using the established effect that increasing mental imagery improves recall of words. Statistical mediation analysis led to conclusions about mediation that were consistent with established theory that increased imagery leads to increased word recall. Benchmark validation based on established substantive theory is discussed as a general way to investigate characteristics of statistical models and a complement to mathematical proof and statistical simulation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Statistical evaluation of diagnostic performance topics in ROC analysis

CERN Document Server

Zou, Kelly H; Bandos, Andriy I; Ohno-Machado, Lucila; Rockette, Howard E

2016-01-01

Statistical evaluation of diagnostic performance in general and Receiver Operating Characteristic (ROC) analysis in particular are important for assessing the performance of medical tests and statistical classifiers, as well as for evaluating predictive models or algorithms. This book presents innovative approaches in ROC analysis, which are relevant to a wide variety of applications, including medical imaging, cancer research, epidemiology, and bioinformatics. Statistical Evaluation of Diagnostic Performance: Topics in ROC Analysis covers areas including monotone-transformation techniques in parametric ROC analysis, ROC methods for combined and pooled biomarkers, Bayesian hierarchical transformation models, sequential designs and inferences in the ROC setting, predictive modeling, multireader ROC analysis, and free-response ROC (FROC) methodology. The book is suitable for graduate-level students and researchers in statistics, biostatistics, epidemiology, public health, biomedical engineering, radiology, medi...

SeDA: A software package for the statistical analysis of the instrument drift

International Nuclear Information System (INIS)

Lee, H. J.; Jang, S. C.; Lim, T. J.

2006-01-01

The setpoints for safety-related equipment are affected by many sources of an uncertainty. ANSI/ISA-S67.04.01-2000 [1] and ISA-RP6 7.04.02-2000 [2] suggested the statistical approaches for ensuring that the safety-related instrument setpoints were established and maintained within the technical specification limits [3]. However, Jang et al. [4] indicated that the preceding methodologies for a setpoint drift analysis might be insufficient to manage a setpoint drift on an instrumentation device and proposed new statistical analysis procedures for the management of a setpoint drift, based on the plant specific as-found/as-left data. Although IHPA (Instrument History Performance Analysis) is a widely known commercial software package to analyze an instrument setpoint drift, several steps in the new procedure cannot be performed by using it because it is based on the statistical approaches suggested in the ANSI/ISA-S67.04.01 -2000 [1] and ISA-RP67.04.02-2000 [2], In this paper we present a software package (SeDA: Setpoint Drift Analysis) that implements new methodologies, and which is easy to use, as it is accompanied by powerful graphical tools. (authors)

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

Science.gov (United States)

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

2013-02-01

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

Bayesian Inference in Statistical Analysis

CERN Document Server

Box, George E P

2011-01-01

The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson The Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences Rob

Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment

Directory of Open Access Journals (Sweden)

Sungju Lee

2016-09-01

Full Text Available A fundamental key for enterprise users is a cloud-based parameter-driven statistical service and it has become a substantial impact on companies worldwide. In this paper, we demonstrate the statistical analysis for some certain criteria that are related to data and applied to the cloud server for a comparison of results. In addition, we present a statistical analysis and cloud-based resource allocation method for a heterogeneous platform environment by performing a data and information analysis with consideration of the application workload and the server capacity, and subsequently propose a service prediction model using a polynomial regression model. In particular, our aim is to provide stable service in a given large-scale enterprise cloud computing environment. The virtual machines (VMs for cloud-based services are assigned to each server with a special methodology to satisfy the uniform utilization distribution model. It is also implemented between users and the platform, which is a main idea of our cloud computing system. Based on the experimental results, we confirm that our prediction model can provide sufficient resources for statistical services to large-scale users while satisfying the uniform utilization distribution.

A statistical test for outlier identification in data envelopment analysis

Directory of Open Access Journals (Sweden)

Morteza Khodabin

2010-09-01

Full Text Available In the use of peer group data to assess individual, typical or best practice performance, the effective detection of outliers is critical for achieving useful results. In these ‘‘deterministic’’ frontier models, statistical theory is now mostly available. This paper deals with the statistical pared sample method and its capability of detecting outliers in data envelopment analysis. In the presented method, each observation is deleted from the sample once and the resulting linear program is solved, leading to a distribution of efficiency estimates. Based on the achieved distribution, a pared test is designed to identify the potential outlier(s. We illustrate the method through a real data set. The method could be used in a first step, as an exploratory data analysis, before using any frontier estimation.

Analysis of Variance: What Is Your Statistical Software Actually Doing?

Science.gov (United States)

Li, Jian; Lomax, Richard G.

2011-01-01

Users assume statistical software packages produce accurate results. In this article, the authors systematically examined Statistical Package for the Social Sciences (SPSS) and Statistical Analysis System (SAS) for 3 analysis of variance (ANOVA) designs, mixed-effects ANOVA, fixed-effects analysis of covariance (ANCOVA), and nested ANOVA. For each…

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

International Nuclear Information System (INIS)

Bakraji, E. H.

2007-01-01

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

Fracture criterion for brittle materials based on statistical cells of finite volume

International Nuclear Information System (INIS)

Cords, H.; Kleist, G.; Zimmermann, R.

1986-06-01

An analytical consideration of the Weibull Statistical Analysis of brittle materials established the necessity of including one additional material constant for a more comprehensive description of the failure behaviour. The Weibull analysis is restricted to infinitesimal volume elements in consequence of the differential calculus applied. It was found that infinitesimally small elements are in conflict with the basic statistical assumption and that the differential calculus is not needed in fact since nowadays most of the stress analyses are based on finite element calculations, and these are most suitable for a subsequent statistical analysis of strength. The size of a finite statistical cell has been introduced as the third material parameter. It should represent the minimum volume containing all statistical features of the material such as distribution of pores, flaws and grains. The new approach also contains a unique treatment of failure under multiaxial stresses. The quantity responsible for failure under multiaxial stresses is introduced as a modified strain energy. Sixteen different tensile specimens including CT-specimens have been investigated experimentally and analyzed with the probabilistic fracture criterion. As a result it can be stated that the failure rates of all types of specimens made from three different grades of graphite are predictable. The accuracy of the prediction is one standard deviation. (orig.) [de

Comparing Visual and Statistical Analysis of Multiple Baseline Design Graphs.

Science.gov (United States)

Wolfe, Katie; Dickenson, Tammiee S; Miller, Bridget; McGrath, Kathleen V

2018-04-01

A growing number of statistical analyses are being developed for single-case research. One important factor in evaluating these methods is the extent to which each corresponds to visual analysis. Few studies have compared statistical and visual analysis, and information about more recently developed statistics is scarce. Therefore, our purpose was to evaluate the agreement between visual analysis and four statistical analyses: improvement rate difference (IRD); Tau-U; Hedges, Pustejovsky, Shadish (HPS) effect size; and between-case standardized mean difference (BC-SMD). Results indicate that IRD and BC-SMD had the strongest overall agreement with visual analysis. Although Tau-U had strong agreement with visual analysis on raw values, it had poorer agreement when those values were dichotomized to represent the presence or absence of a functional relation. Overall, visual analysis appeared to be more conservative than statistical analysis, but further research is needed to evaluate the nature of these disagreements.

Statistical Analysis and Comparison of Harmonics Measured in Offshore Wind Farms

DEFF Research Database (Denmark)

Kocewiak, Lukasz Hubert; Hjerrild, Jesper; Bak, Claus Leth

2011-01-01

The paper shows statistical analysis of harmonic components measured in different offshore wind farms. Harmonic analysis is a complex task and requires many aspects, such as measurements, data processing, modeling, validation, to be taken into consideration. The paper describes measurement process...... and shows sophisticated analysis on representative harmonic measurements from Avedøre Holme, Gunfleet Sands and Burbo Bank wind farms. The nature of generation and behavior of harmonic components in offshore wind farms clearly presented and explained based on probabilistic approach. Some issues regarding...... commonly applied standards are also put forward in the discussion. Based on measurements and data analysis it is shown that a general overview about wind farm harmonic behaviour cannot be fully observed only based on single-value measurements as suggested in the standards but using more descriptive...

Sensitivity analysis and related analysis : A survey of statistical techniques

NARCIS (Netherlands)

Kleijnen, J.P.C.

1995-01-01

This paper reviews the state of the art in five related types of analysis, namely (i) sensitivity or what-if analysis, (ii) uncertainty or risk analysis, (iii) screening, (iv) validation, and (v) optimization. The main question is: when should which type of analysis be applied; which statistical

Statistical and machine learning approaches for network analysis

CERN Document Server

Dehmer, Matthias

2012-01-01

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

Visual wetness perception based on image color statistics.

Science.gov (United States)

Sawayama, Masataka; Adelson, Edward H; Nishida, Shin'ya

2017-05-01

Color vision provides humans and animals with the abilities to discriminate colors based on the wavelength composition of light and to determine the location and identity of objects of interest in cluttered scenes (e.g., ripe fruit among foliage). However, we argue that color vision can inform us about much more than color alone. Since a trichromatic image carries more information about the optical properties of a scene than a monochromatic image does, color can help us recognize complex material qualities. Here we show that human vision uses color statistics of an image for the perception of an ecologically important surface condition (i.e., wetness). Psychophysical experiments showed that overall enhancement of chromatic saturation, combined with a luminance tone change that increases the darkness and glossiness of the image, tended to make dry scenes look wetter. Theoretical analysis along with image analysis of real objects indicated that our image transformation, which we call the wetness enhancing transformation, is consistent with actual optical changes produced by surface wetting. Furthermore, we found that the wetness enhancing transformation operator was more effective for the images with many colors (large hue entropy) than for those with few colors (small hue entropy). The hue entropy may be used to separate surface wetness from other surface states having similar optical properties. While surface wetness and surface color might seem to be independent, there are higher order color statistics that can influence wetness judgments, in accord with the ecological statistics. The present findings indicate that the visual system uses color image statistics in an elegant way to help estimate the complex physical status of a scene.

A Third Moment Adjusted Test Statistic for Small Sample Factor Analysis.

Science.gov (United States)

Lin, Johnny; Bentler, Peter M

2012-01-01

Goodness of fit testing in factor analysis is based on the assumption that the test statistic is asymptotically chi-square; but this property may not hold in small samples even when the factors and errors are normally distributed in the population. Robust methods such as Browne's asymptotically distribution-free method and Satorra Bentler's mean scaling statistic were developed under the presumption of non-normality in the factors and errors. This paper finds new application to the case where factors and errors are normally distributed in the population but the skewness of the obtained test statistic is still high due to sampling error in the observed indicators. An extension of Satorra Bentler's statistic is proposed that not only scales the mean but also adjusts the degrees of freedom based on the skewness of the obtained test statistic in order to improve its robustness under small samples. A simple simulation study shows that this third moment adjusted statistic asymptotically performs on par with previously proposed methods, and at a very small sample size offers superior Type I error rates under a properly specified model. Data from Mardia, Kent and Bibby's study of students tested for their ability in five content areas that were either open or closed book were used to illustrate the real-world performance of this statistic.

[Design and implementation of online statistical analysis function in information system of air pollution and health impact monitoring].

Science.gov (United States)

Lü, Yiran; Hao, Shuxin; Zhang, Guoqing; Liu, Jie; Liu, Yue; Xu, Dongqun

2018-01-01

To implement the online statistical analysis function in information system of air pollution and health impact monitoring, and obtain the data analysis information real-time. Using the descriptive statistical method as well as time-series analysis and multivariate regression analysis, SQL language and visual tools to implement online statistical analysis based on database software. Generate basic statistical tables and summary tables of air pollution exposure and health impact data online; Generate tendency charts of each data part online and proceed interaction connecting to database; Generate butting sheets which can lead to R, SAS and SPSS directly online. The information system air pollution and health impact monitoring implements the statistical analysis function online, which can provide real-time analysis result to its users.

Analysis and Comprehensive Analytical Modeling of Statistical Variations in Subthreshold MOSFET's High Frequency Characteristics

Directory of Open Access Journals (Sweden)

Rawid Banchuin

2014-01-01

Full Text Available In this research, the analysis of statistical variations in subthreshold MOSFET's high frequency characteristics defined in terms of gate capacitance and transition frequency, have been shown and the resulting comprehensive analytical models of such variations in terms of their variances have been proposed. Major imperfection in the physical level properties including random dopant fluctuation and effects of variations in MOSFET's manufacturing process, have been taken into account in the proposed analysis and modeling. The up to dated comprehensive analytical model of statistical variation in MOSFET's parameter has been used as the basis of analysis and modeling. The resulting models have been found to be both analytic and comprehensive as they are the precise mathematical expressions in terms of physical level variables of MOSFET. Furthermore, they have been verified at the nanometer level by using 65~nm level BSIM4 based benchmarks and have been found to be very accurate with smaller than 5 % average percentages of errors. Hence, the performed analysis gives the resulting models which have been found to be the potential mathematical tool for the statistical and variability aware analysis and design of subthreshold MOSFET based VHF circuits, systems and applications.

The effect of image enhancement on the statistical analysis of functional neuroimages : Wavelet-based denoising and Gaussian smoothing

NARCIS (Netherlands)

Wink, AM; Roerdink, JBTM; Sonka, M; Fitzpatrick, JM

2003-01-01

The quality of statistical analyses of functional neuroimages is studied after applying various preprocessing methods. We present wavelet-based denoising as an alternative to Gaussian smoothing, the standard denoising method in statistical parametric mapping (SPM). The wavelet-based denoising

Perceptual and statistical analysis of cardiac phase and amplitude images

International Nuclear Information System (INIS)

Houston, A.; Craig, A.

1991-01-01

A perceptual experiment was conducted using cardiac phase and amplitude images. Estimates of statistical parameters were derived from the images and the diagnostic potential of human and statistical decisions compared. Five methods were used to generate the images from 75 gated cardiac studies, 39 of which were classified as pathological. The images were presented to 12 observers experienced in nuclear medicine. The observers rated the images using a five-category scale based on their confidence of an abnormality presenting. Circular and linear statistics were used to analyse phase and amplitude image data, respectively. Estimates of mean, standard deviation (SD), skewness, kurtosis and the first term of the spatial correlation function were evaluated in the region of the left ventricle. A receiver operating characteristic analysis was performed on both sets of data and the human and statistical decisions compared. For phase images, circular SD was shown to discriminate better between normal and abnormal than experienced observers, but no single statistic discriminated as well as the human observer for amplitude images. (orig.)

Is Cognitive Activity of Speech Based On Statistical Independence?

DEFF Research Database (Denmark)

Feng, Ling; Hansen, Lars Kai

2008-01-01

This paper explores the generality of COgnitive Component Analysis (COCA), which is defined as the process of unsupervised grouping of data such that the ensuing group structure is well-aligned with that resulting from human cognitive activity. The hypothesis of {COCA} is ecological......: the essentially independent features in a context defined ensemble can be efficiently coded using a sparse independent component representation. Our devised protocol aims at comparing the performance of supervised learning (invoking cognitive activity) and unsupervised learning (statistical regularities) based...... on similar representations, and the only difference lies in the human inferred labels. Inspired by the previous research on COCA, we introduce a new pair of models, which directly employ the independent hypothesis. Statistical regularities are revealed at multiple time scales on phoneme, gender, age...

SWToolbox: A surface-water tool-box for statistical analysis of streamflow time series

Science.gov (United States)

Kiang, Julie E.; Flynn, Kate; Zhai, Tong; Hummel, Paul; Granato, Gregory

2018-03-07

This report is a user guide for the low-flow analysis methods provided with version 1.0 of the Surface Water Toolbox (SWToolbox) computer program. The software combines functionality from two software programs—U.S. Geological Survey (USGS) SWSTAT and U.S. Environmental Protection Agency (EPA) DFLOW. Both of these programs have been used primarily for computation of critical low-flow statistics. The main analysis methods are the computation of hydrologic frequency statistics such as the 7-day minimum flow that occurs on average only once every 10 years (7Q10), computation of design flows including biologically based flows, and computation of flow-duration curves and duration hydrographs. Other annual, monthly, and seasonal statistics can also be computed. The interface facilitates retrieval of streamflow discharge data from the USGS National Water Information System and outputs text reports for a record of the analysis. Tools for graphing data and screening tests are available to assist the analyst in conducting the analysis.

Explorations in Statistics: The Analysis of Change

Science.gov (United States)

Curran-Everett, Douglas; Williams, Calvin L.

2015-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This tenth installment of "Explorations in Statistics" explores the analysis of a potential change in some physiological response. As researchers, we often express absolute change as percent change so we can…

Algorithm for image retrieval based on edge gradient orientation statistical code.

Science.gov (United States)

Zeng, Jiexian; Zhao, Yonggang; Li, Weiye; Fu, Xiang

2014-01-01

Image edge gradient direction not only contains important information of the shape, but also has a simple, lower complexity characteristic. Considering that the edge gradient direction histograms and edge direction autocorrelogram do not have the rotation invariance, we put forward the image retrieval algorithm which is based on edge gradient orientation statistical code (hereinafter referred to as EGOSC) by sharing the application of the statistics method in the edge direction of the chain code in eight neighborhoods to the statistics of the edge gradient direction. Firstly, we construct the n-direction vector and make maximal summation restriction on EGOSC to make sure this algorithm is invariable for rotation effectively. Then, we use Euclidean distance of edge gradient direction entropy to measure shape similarity, so that this method is not sensitive to scaling, color, and illumination change. The experimental results and the algorithm analysis demonstrate that the algorithm can be used for content-based image retrieval and has good retrieval results.

Common pitfalls in statistical analysis: “P” values, statistical significance and confidence intervals

Science.gov (United States)

Ranganathan, Priya; Pramesh, C. S.; Buyse, Marc

2015-01-01

In the second part of a series on pitfalls in statistical analysis, we look at various ways in which a statistically significant study result can be expressed. We debunk some of the myths regarding the ‘P’ value, explain the importance of ‘confidence intervals’ and clarify the importance of including both values in a paper PMID:25878958

An introduction to statistical computing a simulation-based approach

CERN Document Server

Voss, Jochen

2014-01-01

A comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems.  Sampling-based simulation techniques are now an invaluable tool for exploring statistical models.  This book gives a comprehensive introduction to the exciting area of sampling-based methods. An Introduction to Statistical Computing introduces the classical topics of random number generation and Monte Carlo methods.  It also includes some advanced met

Application of a statistical thermal design procedure to evaluate the PWR DNBR safety analysis limits

International Nuclear Information System (INIS)

Robeyns, J.; Parmentier, F.; Peeters, G.

2001-01-01

In the framework of safety analysis for the Belgian nuclear power plants and for the reload compatibility studies, Tractebel Energy Engineering (TEE) has developed, to define a 95/95 DNBR criterion, a statistical thermal design method based on the analytical full statistical approach: the Statistical Thermal Design Procedure (STDP). In that methodology, each DNBR value in the core assemblies is calculated with an adapted CHF (Critical Heat Flux) correlation implemented in the sub-channel code Cobra for core thermal hydraulic analysis. The uncertainties of the correlation are represented by the statistical parameters calculated from an experimental database. The main objective of a sub-channel analysis is to prove that in all class 1 and class 2 situations, the minimum DNBR (Departure from Nucleate Boiling Ratio) remains higher than the Safety Analysis Limit (SAL). The SAL value is calculated from the Statistical Design Limit (SDL) value adjusted with some penalties and deterministic factors. The search of a realistic value for the SDL is the objective of the statistical thermal design methods. In this report, we apply a full statistical approach to define the DNBR criterion or SDL (Statistical Design Limit) with the strict observance of the design criteria defined in the Standard Review Plan. The same statistical approach is used to define the expected number of rods experiencing DNB. (author)

Diagnosis of students' ability in a statistical course based on Rasch probabilistic outcome

Science.gov (United States)

Mahmud, Zamalia; Ramli, Wan Syahira Wan; Sapri, Shamsiah; Ahmad, Sanizah

2017-06-01

Measuring students' ability and performance are important in assessing how well students have learned and mastered the statistical courses. Any improvement in learning will depend on the student's approaches to learning, which are relevant to some factors of learning, namely assessment methods carrying out tasks consisting of quizzes, tests, assignment and final examination. This study has attempted an alternative approach to measure students' ability in an undergraduate statistical course based on the Rasch probabilistic model. Firstly, this study aims to explore the learning outcome patterns of students in a statistics course (Applied Probability and Statistics) based on an Entrance-Exit survey. This is followed by investigating students' perceived learning ability based on four Course Learning Outcomes (CLOs) and students' actual learning ability based on their final examination scores. Rasch analysis revealed that students perceived themselves as lacking the ability to understand about 95% of the statistics concepts at the beginning of the class but eventually they had a good understanding at the end of the 14 weeks class. In terms of students' performance in their final examination, their ability in understanding the topics varies at different probability values given the ability of the students and difficulty of the questions. Majority found the probability and counting rules topic to be the most difficult to learn.

Improvement of Information and Methodical Provision of Macro-economic Statistical Analysis

Directory of Open Access Journals (Sweden)

Tiurina Dina M.

2014-02-01

Full Text Available The article generalises and analyses main shortcomings of the modern system of macro-statistical analysis based on the use of the system of national accounts and balance of the national economy. The article proves on the basis of historic analysis of formation of indicators of the system of national accounts that problems with its practical use have both regional and global reasons. In order to eliminate impossibility of accounting life quality the article offers a system of quality indicators based on the general perception of wellbeing as assurance in own solvency of population and representative sampling of economic subjects.

Analysis of uncertainties and convergence of the statistical quantities in turbulent wall-bounded flows by means of a physically based criterion

Science.gov (United States)

Andrade, João Rodrigo; Martins, Ramon Silva; Thompson, Roney Leon; Mompean, Gilmar; da Silveira Neto, Aristeu

2018-04-01

The present paper provides an analysis of the statistical uncertainties associated with direct numerical simulation (DNS) results and experimental data for turbulent channel and pipe flows, showing a new physically based quantification of these errors, to improve the determination of the statistical deviations between DNSs and experiments. The analysis is carried out using a recently proposed criterion by Thompson et al. ["A methodology to evaluate statistical errors in DNS data of plane channel flows," Comput. Fluids 130, 1-7 (2016)] for fully turbulent plane channel flows, where the mean velocity error is estimated by considering the Reynolds stress tensor, and using the balance of the mean force equation. It also presents how the residual error evolves in time for a DNS of a plane channel flow, and the influence of the Reynolds number on its convergence rate. The root mean square of the residual error is shown in order to capture a single quantitative value of the error associated with the dimensionless averaging time. The evolution in time of the error norm is compared with the final error provided by DNS data of similar Reynolds numbers available in the literature. A direct consequence of this approach is that it was possible to compare different numerical results and experimental data, providing an improved understanding of the convergence of the statistical quantities in turbulent wall-bounded flows.

Categorical data processing for real estate objects valuation using statistical analysis

Science.gov (United States)

Parygin, D. S.; Malikov, V. P.; Golubev, A. V.; Sadovnikova, N. P.; Petrova, T. M.; Finogeev, A. G.

2018-05-01

Theoretical and practical approaches to the use of statistical methods for studying various properties of infrastructure objects are analyzed in the paper. Methods of forecasting the value of objects are considered. A method for coding categorical variables describing properties of real estate objects is proposed. The analysis of the results of modeling the price of real estate objects using regression analysis and an algorithm based on a comparative approach is carried out.

TECHNIQUE OF THE STATISTICAL ANALYSIS OF INVESTMENT APPEAL OF THE REGION

Directory of Open Access Journals (Sweden)

А. А. Vershinina

2014-01-01

Full Text Available The technique of the statistical analysis of investment appeal of the region is given in scientific article for direct foreign investments. Definition of a technique of the statistical analysis is given, analysis stages reveal, the mathematico-statistical tools are considered.

The Statistical Analysis Techniques to Support the NGNP Fuel Performance Experiments

International Nuclear Information System (INIS)

Pham, Bihn T.; Einerson, Jeffrey J.

2010-01-01

This paper describes the development and application of statistical analysis techniques to support the AGR experimental program on NGNP fuel performance. The experiments conducted in the Idaho National Laboratory's Advanced Test Reactor employ fuel compacts placed in a graphite cylinder shrouded by a steel capsule. The tests are instrumented with thermocouples embedded in graphite blocks and the target quantity (fuel/graphite temperature) is regulated by the He-Ne gas mixture that fills the gap volume. Three techniques for statistical analysis, namely control charting, correlation analysis, and regression analysis, are implemented in the SAS-based NGNP Data Management and Analysis System (NDMAS) for automated processing and qualification of the AGR measured data. The NDMAS also stores daily neutronic (power) and thermal (heat transfer) code simulation results along with the measurement data, allowing for their combined use and comparative scrutiny. The ultimate objective of this work includes (a) a multi-faceted system for data monitoring and data accuracy testing, (b) identification of possible modes of diagnostics deterioration and changes in experimental conditions, (c) qualification of data for use in code validation, and (d) identification and use of data trends to support effective control of test conditions with respect to the test target. Analysis results and examples given in the paper show the three statistical analysis techniques providing a complementary capability to warn of thermocouple failures. It also suggests that the regression analysis models relating calculated fuel temperatures and thermocouple readings can enable online regulation of experimental parameters (i.e. gas mixture content), to effectively maintain the target quantity (fuel temperature) within a given range.

Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry

CERN Document Server

Mertens, Bart

2017-01-01

This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass ...

Statistical methods for data analysis in particle physics

CERN Document Server

AUTHOR|(CDS)2070643

2015-01-01

This concise set of course-based notes provides the reader with the main concepts and tools to perform statistical analysis of experimental data, in particular in the field of high-energy physics (HEP). First, an introduction to probability theory and basic statistics is given, mainly as reminder from advanced undergraduate studies, yet also in view to clearly distinguish the Frequentist versus Bayesian approaches and interpretations in subsequent applications. More advanced concepts and applications are gradually introduced, culminating in the chapter on upper limits as many applications in HEP concern hypothesis testing, where often the main goal is to provide better and better limits so as to be able to distinguish eventually between competing hypotheses or to rule out some of them altogether. Many worked examples will help newcomers to the field and graduate students to understand the pitfalls in applying theoretical concepts to actual data

PROSA: A computer program for statistical analysis of near-real-time-accountancy (NRTA) data

International Nuclear Information System (INIS)

Beedgen, R.; Bicking, U.

1987-04-01

The computer program PROSA (Program for Statistical Analysis of NRTA Data) is a tool to decide on the basis of statistical considerations if, in a given sequence of materials balance periods, a loss of material might have occurred or not. The evaluation of the material balance data is based on statistical test procedures. In PROSA three truncated sequential tests are applied to a sequence of material balances. The manual describes the statistical background of PROSA and how to use the computer program on an IBM-PC with DOS 3.1. (orig.) [de

Statistical analysis of network data with R

CERN Document Server

Kolaczyk, Eric D

2014-01-01

Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).

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

International Nuclear Information System (INIS)

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

2009-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2009-11-15

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

FADTTS: functional analysis of diffusion tensor tract statistics.

Science.gov (United States)

Zhu, Hongtu; Kong, Linglong; Li, Runze; Styner, Martin; Gerig, Guido; Lin, Weili; Gilmore, John H

2011-06-01

The aim of this paper is to present a functional analysis of a diffusion tensor tract statistics (FADTTS) pipeline for delineating the association between multiple diffusion properties along major white matter fiber bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these white matter tract properties in various diffusion tensor imaging studies. The FADTTS integrates five statistical tools: (i) a multivariate varying coefficient model for allowing the varying coefficient functions in terms of arc length to characterize the varying associations between fiber bundle diffusion properties and a set of covariates, (ii) a weighted least squares estimation of the varying coefficient functions, (iii) a functional principal component analysis to delineate the structure of the variability in fiber bundle diffusion properties, (iv) a global test statistic to test hypotheses of interest, and (v) a simultaneous confidence band to quantify the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FADTTS. We apply FADTTS to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment. FADTTS can be used to facilitate the understanding of normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. The advantages of FADTTS compared with the other existing approaches are that they are capable of modeling the structured inter-subject variability, testing the joint effects, and constructing their simultaneous confidence bands. However, FADTTS is not crucial for estimation and reduces to the functional analysis method for the single measure. Copyright © 2011 Elsevier Inc. All rights reserved.

Obtaining Application-based and Content-based Internet Traffic Statistics

DEFF Research Database (Denmark)

Bujlow, Tomasz; Pedersen, Jens Myrup

2012-01-01

the Volunteer-Based System for Research on the Internet, developed at Aalborg University, is capable of providing detailed statistics of Internet usage. Since an increasing amount of HTTP traffic has been observed during the last few years, the system also supports creating statistics of different kinds of HTTP...... traffic, like audio, video, file transfers, etc. All statistics can be obtained for individual users of the system, for groups of users, or for all users altogether. This paper presents results with real data collected from a limited number of real users over six months. We demonstrate that the system can...

Semiclassical analysis, Witten Laplacians, and statistical mechanis

CERN Document Server

Helffer, Bernard

2002-01-01

This important book explains how the technique of Witten Laplacians may be useful in statistical mechanics. It considers the problem of analyzing the decay of correlations, after presenting its origin in statistical mechanics. In addition, it compares the Witten Laplacian approach with other techniques, such as the transfer matrix approach and its semiclassical analysis. The author concludes by providing a complete proof of the uniform Log-Sobolev inequality. Contents: Witten Laplacians Approach; Problems in Statistical Mechanics with Discrete Spins; Laplace Integrals and Transfer Operators; S

A novel statistic for genome-wide interaction analysis.

Directory of Open Access Journals (Sweden)

Xuesen Wu

2010-09-01

Full Text Available Although great progress in genome-wide association studies (GWAS has been made, the significant SNP associations identified by GWAS account for only a few percent of the genetic variance, leading many to question where and how we can find the missing heritability. There is increasing interest in genome-wide interaction analysis as a possible source of finding heritability unexplained by current GWAS. However, the existing statistics for testing interaction have low power for genome-wide interaction analysis. To meet challenges raised by genome-wide interactional analysis, we have developed a novel statistic for testing interaction between two loci (either linked or unlinked. The null distribution and the type I error rates of the new statistic for testing interaction are validated using simulations. Extensive power studies show that the developed statistic has much higher power to detect interaction than classical logistic regression. The results identified 44 and 211 pairs of SNPs showing significant evidence of interactions with FDR<0.001 and 0.001analysis is a valuable tool for finding remaining missing heritability unexplained by the current GWAS, and the developed novel statistic is able to search significant interaction between SNPs across the genome. Real data analysis showed that the results of genome-wide interaction analysis can be replicated in two independent studies.

Natural disaster risk analysis for critical infrastructure systems: An approach based on statistical learning theory

International Nuclear Information System (INIS)

Guikema, Seth D.

2009-01-01

Probabilistic risk analysis has historically been developed for situations in which measured data about the overall reliability of a system are limited and expert knowledge is the best source of information available. There continue to be a number of important problem areas characterized by a lack of hard data. However, in other important problem areas the emergence of information technology has transformed the situation from one characterized by little data to one characterized by data overabundance. Natural disaster risk assessments for events impacting large-scale, critical infrastructure systems such as electric power distribution systems, transportation systems, water supply systems, and natural gas supply systems are important examples of problems characterized by data overabundance. There are often substantial amounts of information collected and archived about the behavior of these systems over time. Yet it can be difficult to effectively utilize these large data sets for risk assessment. Using this information for estimating the probability or consequences of system failure requires a different approach and analysis paradigm than risk analysis for data-poor systems does. Statistical learning theory, a diverse set of methods designed to draw inferences from large, complex data sets, can provide a basis for risk analysis for data-rich systems. This paper provides an overview of statistical learning theory methods and discusses their potential for greater use in risk analysis

Neutron activation and statistical analysis of pottery from Thera, Greece

International Nuclear Information System (INIS)

Kilikoglou, V.; Grimanis, A.P.; Karayannis, M.I.

1990-01-01

Neutron activation analysis, in combination with multivariate analysis of the generated data, was used for the chemical characterization of prehistoric pottery from the Greek islands of Thera, Melos (islands with similar geology) and Crete. The statistical procedure which proved that Theran pottery could be distinguished from Melian is described. This discrimination, attained for the first time, was mainly based on the concentrations of the trace elements Sm, Yb, Lu and Cr. Also, Cretan imports to both Thera and Melos were clearly separable from local products. (author) 22 refs.; 1 fig.; 4 tabs

A spatial scan statistic for survival data based on Weibull distribution.

Science.gov (United States)

Bhatt, Vijaya; Tiwari, Neeraj

2014-05-20

The spatial scan statistic has been developed as a geographical cluster detection analysis tool for different types of data sets such as Bernoulli, Poisson, ordinal, normal and exponential. We propose a scan statistic for survival data based on Weibull distribution. It may also be used for other survival distributions, such as exponential, gamma, and log normal. The proposed method is applied on the survival data of tuberculosis patients for the years 2004-2005 in Nainital district of Uttarakhand, India. Simulation studies reveal that the proposed method performs well for different survival distribution functions. Copyright © 2013 John Wiley & Sons, Ltd.

Statistical performance and information content of time lag analysis and redundancy analysis in time series modeling.

Science.gov (United States)

Angeler, David G; Viedma, Olga; Moreno, José M

2009-11-01

Time lag analysis (TLA) is a distance-based approach used to study temporal dynamics of ecological communities by measuring community dissimilarity over increasing time lags. Despite its increased use in recent years, its performance in comparison with other more direct methods (i.e., canonical ordination) has not been evaluated. This study fills this gap using extensive simulations and real data sets from experimental temporary ponds (true zooplankton communities) and landscape studies (landscape categories as pseudo-communities) that differ in community structure and anthropogenic stress history. Modeling time with a principal coordinate of neighborhood matrices (PCNM) approach, the canonical ordination technique (redundancy analysis; RDA) consistently outperformed the other statistical tests (i.e., TLAs, Mantel test, and RDA based on linear time trends) using all real data. In addition, the RDA-PCNM revealed different patterns of temporal change, and the strength of each individual time pattern, in terms of adjusted variance explained, could be evaluated, It also identified species contributions to these patterns of temporal change. This additional information is not provided by distance-based methods. The simulation study revealed better Type I error properties of the canonical ordination techniques compared with the distance-based approaches when no deterministic component of change was imposed on the communities. The simulation also revealed that strong emphasis on uniform deterministic change and low variability at other temporal scales is needed to result in decreased statistical power of the RDA-PCNM approach relative to the other methods. Based on the statistical performance of and information content provided by RDA-PCNM models, this technique serves ecologists as a powerful tool for modeling temporal change of ecological (pseudo-) communities.

Towards an Industrial Application of Statistical Uncertainty Analysis Methods to Multi-physical Modelling and Safety Analyses

International Nuclear Information System (INIS)

Zhang, Jinzhao; Segurado, Jacobo; Schneidesch, Christophe

2013-01-01

Since 1980's, Tractebel Engineering (TE) has being developed and applied a multi-physical modelling and safety analyses capability, based on a code package consisting of the best estimate 3D neutronic (PANTHER), system thermal hydraulic (RELAP5), core sub-channel thermal hydraulic (COBRA-3C), and fuel thermal mechanic (FRAPCON/FRAPTRAN) codes. A series of methodologies have been developed to perform and to license the reactor safety analysis and core reload design, based on the deterministic bounding approach. Following the recent trends in research and development as well as in industrial applications, TE has been working since 2010 towards the application of the statistical sensitivity and uncertainty analysis methods to the multi-physical modelling and licensing safety analyses. In this paper, the TE multi-physical modelling and safety analyses capability is first described, followed by the proposed TE best estimate plus statistical uncertainty analysis method (BESUAM). The chosen statistical sensitivity and uncertainty analysis methods (non-parametric order statistic method or bootstrap) and tool (DAKOTA) are then presented, followed by some preliminary results of their applications to FRAPCON/FRAPTRAN simulation of OECD RIA fuel rod codes benchmark and RELAP5/MOD3.3 simulation of THTF tests. (authors)

The effect of project-based learning on students' statistical literacy levels for data representation

Science.gov (United States)

Koparan, Timur; Güven, Bülent

2015-07-01

The point of this study is to define the effect of project-based learning approach on 8th Grade secondary-school students' statistical literacy levels for data representation. To achieve this goal, a test which consists of 12 open-ended questions in accordance with the views of experts was developed. Seventy 8th grade secondary-school students, 35 in the experimental group and 35 in the control group, took this test twice, one before the application and one after the application. All the raw scores were turned into linear points by using the Winsteps 3.72 modelling program that makes the Rasch analysis and t-tests, and an ANCOVA analysis was carried out with the linear points. Depending on the findings, it was concluded that the project-based learning approach increases students' level of statistical literacy for data representation. Students' levels of statistical literacy before and after the application were shown through the obtained person-item maps.

Noise removing in encrypted color images by statistical analysis

Science.gov (United States)

Islam, N.; Puech, W.

2012-03-01

Cryptographic techniques are used to secure confidential data from unauthorized access but these techniques are very sensitive to noise. A single bit change in encrypted data can have catastrophic impact over the decrypted data. This paper addresses the problem of removing bit error in visual data which are encrypted using AES algorithm in the CBC mode. In order to remove the noise, a method is proposed which is based on the statistical analysis of each block during the decryption. The proposed method exploits local statistics of the visual data and confusion/diffusion properties of the encryption algorithm to remove the errors. Experimental results show that the proposed method can be used at the receiving end for the possible solution for noise removing in visual data in encrypted domain.

A statistical approach to plasma profile analysis

International Nuclear Information System (INIS)

Kardaun, O.J.W.F.; McCarthy, P.J.; Lackner, K.; Riedel, K.S.

1990-05-01

A general statistical approach to the parameterisation and analysis of tokamak profiles is presented. The modelling of the profile dependence on both the radius and the plasma parameters is discussed, and pertinent, classical as well as robust, methods of estimation are reviewed. Special attention is given to statistical tests for discriminating between the various models, and to the construction of confidence intervals for the parameterised profiles and the associated global quantities. The statistical approach is shown to provide a rigorous approach to the empirical testing of plasma profile invariance. (orig.)

Interactive statistics with ILLMO

NARCIS (Netherlands)

Martens, J.B.O.S.

2014-01-01

Progress in empirical research relies on adequate statistical analysis and reporting. This article proposes an alternative approach to statistical modeling that is based on an old but mostly forgotten idea, namely Thurstone modeling. Traditional statistical methods assume that either the measured

Study designs, use of statistical tests, and statistical analysis software choice in 2015: Results from two Pakistani monthly Medline indexed journals.

Science.gov (United States)

Shaikh, Masood Ali

2017-09-01

Assessment of research articles in terms of study designs used, statistical tests applied and the use of statistical analysis programmes help determine research activity profile and trends in the country. In this descriptive study, all original articles published by Journal of Pakistan Medical Association (JPMA) and Journal of the College of Physicians and Surgeons Pakistan (JCPSP), in the year 2015 were reviewed in terms of study designs used, application of statistical tests, and the use of statistical analysis programmes. JPMA and JCPSP published 192 and 128 original articles, respectively, in the year 2015. Results of this study indicate that cross-sectional study design, bivariate inferential statistical analysis entailing comparison between two variables/groups, and use of statistical software programme SPSS to be the most common study design, inferential statistical analysis, and statistical analysis software programmes, respectively. These results echo previously published assessment of these two journals for the year 2014.

The Statistical Analysis of Time Series

CERN Document Server

Anderson, T W

2011-01-01

The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences George

Analysis of room transfer function and reverberant signal statistics

DEFF Research Database (Denmark)

Georganti, Eleftheria; Mourjopoulos, John; Jacobsen, Finn

2008-01-01

For some time now, statistical analysis has been a valuable tool in analyzing room transfer functions (RTFs). This work examines existing statistical time-frequency models and techniques for RTF analysis (e.g., Schroeder's stochastic model and the standard deviation over frequency bands for the RTF...... magnitude and phase). RTF fractional octave smoothing, as with 1-slash 3 octave analysis, may lead to RTF simplifications that can be useful for several audio applications, like room compensation, room modeling, auralisation purposes. The aim of this work is to identify the relationship of optimal response...... and the corresponding ratio of the direct and reverberant signal. In addition, this work examines the statistical quantities for speech and audio signals prior to their reproduction within rooms and when recorded in rooms. Histograms and other statistical distributions are used to compare RTF minima of typical...

Fissure formation in coke. 3: Coke size distribution and statistical analysis

Energy Technology Data Exchange (ETDEWEB)

D.R. Jenkins; D.E. Shaw; M.R. Mahoney [CSIRO, North Ryde, NSW (Australia). Mathematical and Information Sciences

2010-07-15

A model of coke stabilization, based on a fundamental model of fissuring during carbonisation is used to demonstrate the applicability of the fissuring model to actual coke size distributions. The results indicate that the degree of stabilization is important in determining the size distribution. A modified form of the Weibull distribution is shown to provide a better representation of the whole coke size distribution compared to the Rosin-Rammler distribution, which is generally only fitted to the lump coke. A statistical analysis of a large number of experiments in a pilot scale coke oven shows reasonably good prediction of the coke mean size, based on parameters related to blend rank, amount of low rank coal, fluidity and ash. However, the prediction of measures of the spread of the size distribution is more problematic. The fissuring model, the size distribution representation and the statistical analysis together provide a comprehensive capability for understanding and predicting the mean size and distribution of coke lumps produced during carbonisation. 12 refs., 16 figs., 4 tabs.

Validation of statistical models for creep rupture by parametric analysis

Energy Technology Data Exchange (ETDEWEB)

Bolton, J., E-mail: john.bolton@uwclub.net [65, Fisher Ave., Rugby, Warks CV22 5HW (United Kingdom)

2012-01-15

Statistical analysis is an efficient method for the optimisation of any candidate mathematical model of creep rupture data, and for the comparative ranking of competing models. However, when a series of candidate models has been examined and the best of the series has been identified, there is no statistical criterion to determine whether a yet more accurate model might be devised. Hence there remains some uncertainty that the best of any series examined is sufficiently accurate to be considered reliable as a basis for extrapolation. This paper proposes that models should be validated primarily by parametric graphical comparison to rupture data and rupture gradient data. It proposes that no mathematical model should be considered reliable for extrapolation unless the visible divergence between model and data is so small as to leave no apparent scope for further reduction. This study is based on the data for a 12% Cr alloy steel used in BS PD6605:1998 to exemplify its recommended statistical analysis procedure. The models considered in this paper include a) a relatively simple model, b) the PD6605 recommended model and c) a more accurate model of somewhat greater complexity. - Highlights: Black-Right-Pointing-Pointer The paper discusses the validation of creep rupture models derived from statistical analysis. Black-Right-Pointing-Pointer It demonstrates that models can be satisfactorily validated by a visual-graphic comparison of models to data. Black-Right-Pointing-Pointer The method proposed utilises test data both as conventional rupture stress and as rupture stress gradient. Black-Right-Pointing-Pointer The approach is shown to be more reliable than a well-established and widely used method (BS PD6605).

Transit safety & security statistics & analysis 2002 annual report (formerly SAMIS)

Science.gov (United States)

2004-12-01

The Transit Safety & Security Statistics & Analysis 2002 Annual Report (formerly SAMIS) is a compilation and analysis of mass transit accident, casualty, and crime statistics reported under the Federal Transit Administrations (FTAs) National Tr...

Transit safety & security statistics & analysis 2003 annual report (formerly SAMIS)

Science.gov (United States)

2005-12-01

The Transit Safety & Security Statistics & Analysis 2003 Annual Report (formerly SAMIS) is a compilation and analysis of mass transit accident, casualty, and crime statistics reported under the Federal Transit Administrations (FTAs) National Tr...

Statistical analysis of the determinations of the Sun's Galactocentric distance

Science.gov (United States)

Malkin, Zinovy

2013-02-01

Based on several tens of R0 measurements made during the past two decades, several studies have been performed to derive the best estimate of R0. Some used just simple averaging to derive a result, whereas others provided comprehensive analyses of possible errors in published results. In either case, detailed statistical analyses of data used were not performed. However, a computation of the best estimates of the Galactic rotation constants is not only an astronomical but also a metrological task. Here we perform an analysis of 53 R0 measurements (published in the past 20 years) to assess the consistency of the data. Our analysis shows that they are internally consistent. It is also shown that any trend in the R0 estimates from the last 20 years is statistically negligible, which renders the presence of a bandwagon effect doubtful. On the other hand, the formal errors in the published R0 estimates improve significantly with time.

Automatic Generation of Algorithms for the Statistical Analysis of Planetary Nebulae Images

Science.gov (United States)

Fischer, Bernd

2004-01-01

Analyzing data sets collected in experiments or by observations is a Core scientific activity. Typically, experimentd and observational data are &aught with uncertainty, and the analysis is based on a statistical model of the conjectured underlying processes, The large data volumes collected by modern instruments make computer support indispensible for this. Consequently, scientists spend significant amounts of their time with the development and refinement of the data analysis programs. AutoBayes [GF+02, FS03] is a fully automatic synthesis system for generating statistical data analysis programs. Externally, it looks like a compiler: it takes an abstract problem specification and translates it into executable code. Its input is a concise description of a data analysis problem in the form of a statistical model as shown in Figure 1; its output is optimized and fully documented C/C++ code which can be linked dynamically into the Matlab and Octave environments. Internally, however, it is quite different: AutoBayes derives a customized algorithm implementing the given model using a schema-based process, and then further refines and optimizes the algorithm into code. A schema is a parameterized code template with associated semantic constraints which define and restrict the template s applicability. The schema parameters are instantiated in a problem-specific way during synthesis as AutoBayes checks the constraints against the original model or, recursively, against emerging sub-problems. AutoBayes schema library contains problem decomposition operators (which are justified by theorems in a formal logic in the domain of Bayesian networks) as well as machine learning algorithms (e.g., EM, k-Means) and nu- meric optimization methods (e.g., Nelder-Mead simplex, conjugate gradient). AutoBayes augments this schema-based approach by symbolic computation to derive closed-form solutions whenever possible. This is a major advantage over other statistical data analysis systems

Statistical Modelling of Wind Proles - Data Analysis and Modelling

DEFF Research Database (Denmark)

Jónsson, Tryggvi; Pinson, Pierre

The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles.......The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles....

Statistical analysis of long term spatial and temporal trends of ...

Indian Academy of Sciences (India)

Statistical analysis of long term spatial and temporal trends of temperature ... CGCM3; HadCM3; modified Mann–Kendall test; statistical analysis; Sutlej basin. ... Water Resources Systems Division, National Institute of Hydrology, Roorkee 247 ...

Statistical reliability assessment of software-based systems

International Nuclear Information System (INIS)

Korhonen, J.; Pulkkinen, U.; Haapanen, P.

1997-01-01

Plant vendors nowadays propose software-based systems even for the most critical safety functions. The reliability estimation of safety critical software-based systems is difficult since the conventional modeling techniques do not necessarily apply to the analysis of these systems, and the quantification seems to be impossible. Due to lack of operational experience and due to the nature of software faults, the conventional reliability estimation methods can not be applied. New methods are therefore needed for the safety assessment of software-based systems. In the research project Programmable automation systems in nuclear power plants (OHA), financed together by the Finnish Centre for Radiation and Nuclear Safety (STUK), the Ministry of Trade and Industry and the Technical Research Centre of Finland (VTT), various safety assessment methods and tools for software based systems are developed and evaluated. This volume in the OHA-report series deals with the statistical reliability assessment of software based systems on the basis of dynamic test results and qualitative evidence from the system design process. Other reports to be published later on in OHA-report series will handle the diversity requirements in safety critical software-based systems, generation of test data from operational profiles and handling of programmable automation in plant PSA-studies. (orig.) (25 refs.)

CORSSA: The Community Online Resource for Statistical Seismicity Analysis

Science.gov (United States)

Michael, Andrew J.; Wiemer, Stefan

2010-01-01

Statistical seismology is the application of rigorous statistical methods to earthquake science with the goal of improving our knowledge of how the earth works. Within statistical seismology there is a strong emphasis on the analysis of seismicity data in order to improve our scientific understanding of earthquakes and to improve the evaluation and testing of earthquake forecasts, earthquake early warning, and seismic hazards assessments. Given the societal importance of these applications, statistical seismology must be done well. Unfortunately, a lack of educational resources and available software tools make it difficult for students and new practitioners to learn about this discipline. The goal of the Community Online Resource for Statistical Seismicity Analysis (CORSSA) is to promote excellence in statistical seismology by providing the knowledge and resources necessary to understand and implement the best practices, so that the reader can apply these methods to their own research. This introduction describes the motivation for and vision of CORRSA. It also describes its structure and contents.

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

International Nuclear Information System (INIS)

Wilson, G.E.

1992-01-01

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

Applied multivariate statistical analysis

CERN Document Server

Härdle, Wolfgang Karl

2015-01-01

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

SOCR: Statistics Online Computational Resource

Directory of Open Access Journals (Sweden)

Ivo D. Dinov

2006-10-01

Full Text Available The need for hands-on computer laboratory experience in undergraduate and graduate statistics education has been firmly established in the past decade. As a result a number of attempts have been undertaken to develop novel approaches for problem-driven statistical thinking, data analysis and result interpretation. In this paper we describe an integrated educational web-based framework for: interactive distribution modeling, virtual online probability experimentation, statistical data analysis, visualization and integration. Following years of experience in statistical teaching at all college levels using established licensed statistical software packages, like STATA, S-PLUS, R, SPSS, SAS, Systat, etc., we have attempted to engineer a new statistics education environment, the Statistics Online Computational Resource (SOCR. This resource performs many of the standard types of statistical analysis, much like other classical tools. In addition, it is designed in a plug-in object-oriented architecture and is completely platform independent, web-based, interactive, extensible and secure. Over the past 4 years we have tested, fine-tuned and reanalyzed the SOCR framework in many of our undergraduate and graduate probability and statistics courses and have evidence that SOCR resources build student's intuition and enhance their learning.

Statistical evaluation of vibration analysis techniques

Science.gov (United States)

Milner, G. Martin; Miller, Patrice S.

1987-01-01

An evaluation methodology is presented for a selection of candidate vibration analysis techniques applicable to machinery representative of the environmental control and life support system of advanced spacecraft; illustrative results are given. Attention is given to the statistical analysis of small sample experiments, the quantification of detection performance for diverse techniques through the computation of probability of detection versus probability of false alarm, and the quantification of diagnostic performance.

HistFitter software framework for statistical data analysis

CERN Document Server

Baak, M.; Côte, D.; Koutsman, A.; Lorenz, J.; Short, D.

2015-01-01

We present a software framework for statistical data analysis, called HistFitter, that has been used extensively by the ATLAS Collaboration to analyze big datasets originating from proton-proton collisions at the Large Hadron Collider at CERN. Since 2012 HistFitter has been the standard statistical tool in searches for supersymmetric particles performed by ATLAS. HistFitter is a programmable and flexible framework to build, book-keep, fit, interpret and present results of data models of nearly arbitrary complexity. Starting from an object-oriented configuration, defined by users, the framework builds probability density functions that are automatically fitted to data and interpreted with statistical tests. A key innovation of HistFitter is its design, which is rooted in core analysis strategies of particle physics. The concepts of control, signal and validation regions are woven into its very fabric. These are progressively treated with statistically rigorous built-in methods. Being capable of working with mu...

Manipulating measurement scales in medical statistical analysis and data mining: A review of methodologies

Directory of Open Access Journals (Sweden)

Hamid Reza Marateb

2014-01-01

Full Text Available Background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal-variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD. Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables.

Manipulating measurement scales in medical statistical analysis and data mining: A review of methodologies

Science.gov (United States)

Marateb, Hamid Reza; Mansourian, Marjan; Adibi, Peyman; Farina, Dario

2014-01-01

Background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal–variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD). Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables. PMID:24672565

Error analysis of terrestrial laser scanning data by means of spherical statistics and 3D graphs.

Science.gov (United States)

Cuartero, Aurora; Armesto, Julia; Rodríguez, Pablo G; Arias, Pedro

2010-01-01

This paper presents a complete analysis of the positional errors of terrestrial laser scanning (TLS) data based on spherical statistics and 3D graphs. Spherical statistics are preferred because of the 3D vectorial nature of the spatial error. Error vectors have three metric elements (one module and two angles) that were analyzed by spherical statistics. A study case has been presented and discussed in detail. Errors were calculating using 53 check points (CP) and CP coordinates were measured by a digitizer with submillimetre accuracy. The positional accuracy was analyzed by both the conventional method (modular errors analysis) and the proposed method (angular errors analysis) by 3D graphics and numerical spherical statistics. Two packages in R programming language were performed to obtain graphics automatically. The results indicated that the proposed method is advantageous as it offers a more complete analysis of the positional accuracy, such as angular error component, uniformity of the vector distribution, error isotropy, and error, in addition the modular error component by linear statistics.

Detection method of nonlinearity errors by statistical signal analysis in heterodyne Michelson interferometer.

Science.gov (United States)

Hu, Juju; Hu, Haijiang; Ji, Yinghua

2010-03-15

Periodic nonlinearity that ranges from tens of nanometers to a few nanometers in heterodyne interferometer limits its use in high accuracy measurement. A novel method is studied to detect the nonlinearity errors based on the electrical subdivision and the analysis method of statistical signal in heterodyne Michelson interferometer. Under the movement of micropositioning platform with the uniform velocity, the method can detect the nonlinearity errors by using the regression analysis and Jackknife estimation. Based on the analysis of the simulations, the method can estimate the influence of nonlinearity errors and other noises for the dimensions measurement in heterodyne Michelson interferometer.

Analysis and classification of ECG-waves and rhythms using circular statistics and vector strength

Directory of Open Access Journals (Sweden)

Janßen Jan-Dirk

2017-09-01

Full Text Available The most common way to analyse heart rhythm is to calculate the RR-interval and the heart rate variability. For further evaluation, descriptive statistics are often used. Here we introduce a new and more natural heart rhythm analysis tool that is based on circular statistics and vector strength. Vector strength is a tool to measure the periodicity or lack of periodicity of a signal. We divide the signal into non-overlapping window segments and project the detected R-waves around the unit circle using the complex exponential function and the median RR-interval. In addition, we calculate the vector strength and apply circular statistics as wells as an angular histogram on the R-wave vectors. This approach enables an intuitive visualization and analysis of rhythmicity. Our results show that ECG-waves and rhythms can be easily visualized, analysed and classified by circular statistics and vector strength.

Statistical analysis on extreme wave height

Digital Repository Service at National Institute of Oceanography (India)

Teena, N.V.; SanilKumar, V.; Sudheesh, K.; Sajeev, R.

-294. • WAFO (2000) – A MATLAB toolbox for analysis of random waves and loads, Lund University, Sweden, homepage http://www.maths.lth.se/matstat/wafo/,2000. 15    Table 1: Statistical results of data and fitted distribution for cumulative distribution...

An Automated Energy Detection Algorithm Based on Morphological and Statistical Processing Techniques

Science.gov (United States)

2018-01-09

100 kHz, 1 MHz 100 MHz–1 GHz 1 100 kHz 3. Statistical Processing 3.1 Statistical Analysis Statistical analysis is the mathematical science...quantitative terms. In commercial prognostics and diagnostic vibrational monitoring applications , statistical techniques that are mainly used for alarm...Balakrishnan N, editors. Handbook of statistics . Amsterdam (Netherlands): Elsevier Science; 1998. p 555–602; (Order statistics and their applications

Retention of Statistical Concepts in a Preliminary Randomization-Based Introductory Statistics Curriculum

Science.gov (United States)

Tintle, Nathan; Topliff, Kylie; VanderStoep, Jill; Holmes, Vicki-Lynn; Swanson, Todd

2012-01-01

Previous research suggests that a randomization-based introductory statistics course may improve student learning compared to the consensus curriculum. However, it is unclear whether these gains are retained by students post-course. We compared the conceptual understanding of a cohort of students who took a randomization-based curriculum (n = 76)…

Statistical Analysis of Environmental Tritium around Wolsong Site

Energy Technology Data Exchange (ETDEWEB)

Kim, Ju Youl [FNC Technology Co., Yongin (Korea, Republic of)

2010-04-15

To find the relationship among airborne tritium, tritium in rainwater, TFWT (Tissue Free Water Tritium) and TBT (Tissue Bound Tritium), statistical analysis is conducted based on tritium data measured at KHNP employees' house around Wolsong nuclear power plants during 10 years from 1999 to 2008. The results show that tritium in such media exhibits a strong seasonal and annual periodicity. Tritium concentration in rainwater is observed to be highly correlated with TFWT and directly transmitted to TFWT without delay. The response of environmental radioactivity of tritium around Wolsong site is analyzed using time-series technique and non-parametric trend analysis. Tritium in the atmosphere and rainwater is strongly auto-correlated by seasonal and annual periodicity. TFWT concentration in pine needle is proven to be more sensitive to rainfall phenomenon than other weather variables. Non-parametric trend analysis of TFWT concentration within pine needle shows a increasing slope in terms of confidence level of 95%. This study demonstrates a usefulness of time-series and trend analysis for the interpretation of environmental radioactivity relationship with various environmental media.

Statistical Analysis of 30 Years Rainfall Data: A Case Study

Science.gov (United States)

Arvind, G.; Ashok Kumar, P.; Girish Karthi, S.; Suribabu, C. R.

2017-07-01

Rainfall is a prime input for various engineering design such as hydraulic structures, bridges and culverts, canals, storm water sewer and road drainage system. The detailed statistical analysis of each region is essential to estimate the relevant input value for design and analysis of engineering structures and also for crop planning. A rain gauge station located closely in Trichy district is selected for statistical analysis where agriculture is the prime occupation. The daily rainfall data for a period of 30 years is used to understand normal rainfall, deficit rainfall, Excess rainfall and Seasonal rainfall of the selected circle headquarters. Further various plotting position formulae available is used to evaluate return period of monthly, seasonally and annual rainfall. This analysis will provide useful information for water resources planner, farmers and urban engineers to assess the availability of water and create the storage accordingly. The mean, standard deviation and coefficient of variation of monthly and annual rainfall was calculated to check the rainfall variability. From the calculated results, the rainfall pattern is found to be erratic. The best fit probability distribution was identified based on the minimum deviation between actual and estimated values. The scientific results and the analysis paved the way to determine the proper onset and withdrawal of monsoon results which were used for land preparation and sowing.

Incorporating Code-Based Software in an Introductory Statistics Course

Science.gov (United States)

Doehler, Kirsten; Taylor, Laura

2015-01-01

This article is based on the experiences of two statistics professors who have taught students to write and effectively utilize code-based software in a college-level introductory statistics course. Advantages of using software and code-based software in this context are discussed. Suggestions are made on how to ease students into using code with…

Statistical Analysis of Zebrafish Locomotor Response.

Science.gov (United States)

Liu, Yiwen; Carmer, Robert; Zhang, Gaonan; Venkatraman, Prahatha; Brown, Skye Ashton; Pang, Chi-Pui; Zhang, Mingzhi; Ma, Ping; Leung, Yuk Fai

2015-01-01

Zebrafish larvae display rich locomotor behaviour upon external stimulation. The movement can be simultaneously tracked from many larvae arranged in multi-well plates. The resulting time-series locomotor data have been used to reveal new insights into neurobiology and pharmacology. However, the data are of large scale, and the corresponding locomotor behavior is affected by multiple factors. These issues pose a statistical challenge for comparing larval activities. To address this gap, this study has analyzed a visually-driven locomotor behaviour named the visual motor response (VMR) by the Hotelling's T-squared test. This test is congruent with comparing locomotor profiles from a time period. Different wild-type (WT) strains were compared using the test, which shows that they responded differently to light change at different developmental stages. The performance of this test was evaluated by a power analysis, which shows that the test was sensitive for detecting differences between experimental groups with sample numbers that were commonly used in various studies. In addition, this study investigated the effects of various factors that might affect the VMR by multivariate analysis of variance (MANOVA). The results indicate that the larval activity was generally affected by stage, light stimulus, their interaction, and location in the plate. Nonetheless, different factors affected larval activity differently over time, as indicated by a dynamical analysis of the activity at each second. Intriguingly, this analysis also shows that biological and technical repeats had negligible effect on larval activity. This finding is consistent with that from the Hotelling's T-squared test, and suggests that experimental repeats can be combined to enhance statistical power. Together, these investigations have established a statistical framework for analyzing VMR data, a framework that should be generally applicable to other locomotor data with similar structure.

A wind proxy based on migrating dunes at the Baltic coast: statistical analysis of the link between wind conditions and sand movement

Science.gov (United States)

Bierstedt, Svenja E.; Hünicke, Birgit; Zorita, Eduardo; Ludwig, Juliane

2017-07-01

We statistically analyse the relationship between the structure of migrating dunes in the southern Baltic and the driving wind conditions over the past 26 years, with the long-term aim of using migrating dunes as a proxy for past wind conditions at an interannual resolution. The present analysis is based on the dune record derived from geo-radar measurements by Ludwig et al. (2017). The dune system is located at the Baltic Sea coast of Poland and is migrating from west to east along the coast. The dunes present layers with different thicknesses that can be assigned to absolute dates at interannual timescales and put in relation to seasonal wind conditions. To statistically analyse this record and calibrate it as a wind proxy, we used a gridded regional meteorological reanalysis data set (coastDat2) covering recent decades. The identified link between the dune annual layers and wind conditions was additionally supported by the co-variability between dune layers and observed sea level variations in the southern Baltic Sea. We include precipitation and temperature into our analysis, in addition to wind, to learn more about the dependency between these three atmospheric factors and their common influence on the dune system. We set up a statistical linear model based on the correlation between the frequency of days with specific wind conditions in a given season and dune migration velocities derived for that season. To some extent, the dune records can be seen as analogous to tree-ring width records, and hence we use a proxy validation method usually applied in dendrochronology, cross-validation with the leave-one-out method, when the observational record is short. The revealed correlations between the wind record from the reanalysis and the wind record derived from the dune structure is in the range between 0.28 and 0.63, yielding similar statistical validation skill as dendroclimatological records.

Statistical analysis of seismicity and hazard estimation for Italy (mixed approach). Statistical parameters of main shocks and aftershocks in the Italian region

International Nuclear Information System (INIS)

Molchan, G.M.; Kronrod, T.L.; Dmitrieva, O.E.

1995-03-01

The catalog of earthquakes of Italy (1900-1993) is analyzed in the present work. The following problems have been considered: 1) a choice of the operating magnitude, 2) an analysis of data completeness, and 3) a grouping (in time and in space). The catalog has been separated into main shocks and aftershocks. Statistical estimations of seismicity parameters (a,b) are performed for the seismogenetic zones defined by GNDT. The non-standard elements of the analysis performed are: (a) statistical estimation and comparison of seismicity parameters under the condition of arbitrary data grouping in magnitude, time and space; (b) use of a not conventional statistical method for the aftershock identification; the method is based on the idea of optimizing two kinds of errors in the aftershock identification process; (c) use of the aftershock zones to reveal seismically- interrelated seismogenic zones. This procedure contributes to the stability of the estimation of the ''b-value'' Refs, 25 figs, tabs

Perspectives on the application of order-statistics in best-estimate plus uncertainty nuclear safety analysis

International Nuclear Information System (INIS)

Martin, Robert P.; Nutt, William T.

2011-01-01

Research highlights: → Historical recitation on application of order-statistics models to nuclear power plant thermal-hydraulics safety analysis. → Interpretation of regulatory language regarding 10 CFR 50.46 reference to a 'high level of probability'. → Derivation and explanation of order-statistics-based evaluation methodologies considering multi-variate acceptance criteria. → Summary of order-statistics models and recommendations to the nuclear power plant thermal-hydraulics safety analysis community. - Abstract: The application of order-statistics in best-estimate plus uncertainty nuclear safety analysis has received a considerable amount of attention from methodology practitioners, regulators, and academia. At the root of the debate are two questions: (1) what is an appropriate quantitative interpretation of 'high level of probability' in regulatory language appearing in the LOCA rule, 10 CFR 50.46 and (2) how best to mathematically characterize the multi-variate case. An original derivation is offered to provide a quantitative basis for 'high level of probability.' At root of the second question is whether one should recognize a probability statement based on the tolerance region method of Wald and Guba, et al., for multi-variate problems, one explicitly based on the regulatory limits, best articulated in the Wallis-Nutt 'Testing Method', or something else entirely. This paper reviews the origins of the different positions, key assumptions, limitations, and relationship to addressing acceptance criteria. It presents a mathematical interpretation of the regulatory language, including a complete derivation of uni-variate order-statistics (as credited in AREVA's Realistic Large Break LOCA methodology) and extension to multi-variate situations. Lastly, it provides recommendations for LOCA applications, endorsing the 'Testing Method' and addressing acceptance methods allowing for limited sample failures.

Sensitivity analysis of ranked data: from order statistics to quantiles

NARCIS (Netherlands)

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

2015-01-01

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

Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale

Directory of Open Access Journals (Sweden)

Christos Chalkias

2016-03-01

Full Text Available In this paper, an integration landslide susceptibility model by combining expert-based and bivariate statistical analysis (Landslide Susceptibility Index—LSI approaches is presented. Factors related with the occurrence of landslides—such as elevation, slope angle, slope aspect, lithology, land cover, Mean Annual Precipitation (MAP and Peak Ground Acceleration (PGA—were analyzed within a GIS environment. This integrated model produced a landslide susceptibility map which categorized the study area according to the probability level of landslide occurrence. The accuracy of the final map was evaluated by Receiver Operating Characteristics (ROC analysis depending on an independent (validation dataset of landslide events. The prediction ability was found to be 76% revealing that the integration of statistical analysis with human expertise can provide an acceptable landslide susceptibility assessment at regional scale.

ANALYSIS OF STATISTICAL DATA FROM NETWORK INFRASTRUCTURE MONITORING TO DETECT ABNORMAL BEHAVIOR OF SYSTEM LOCAL SEGMENTS

Directory of Open Access Journals (Sweden)

N. A. Bazhayev

2017-01-01

Full Text Available We propose a method of information security monitoring for a wireless network segments of low-power devices, "smart house", "Internet of Things". We have carried out the analysis of characteristics of systems based on wireless technologies, resulting from passive surveillance and active polling of devices that make up the network infrastructure. We have considered a number of external signs of unauthorized access to a wireless network by the potential information security malefactor. The model for analysis of information security conditions is based on the identity, quantity, frequency, and time characteristics. Due to the main features of devices providing network infrastructure, estimation of information security state is directed to the analysis of the system normal operation, rather than the search for signatures and anomalies during performance of various kinds of information attacks. An experiment is disclosed that provides obtaining statistical information on the remote wireless devices, where the accumulation of data for decision-making is done by comparing the statistical information service messages from end nodes in passive and active modes. We present experiment results of the information influence on a typical system. The proposed approach to the analysis of network infrastructure statistical data based on naive Bayesian classifier can be used to determine the state of information security.

Plan delivery quality assurance for CyberKnife: Statistical process control analysis of 350 film-based patient-specific QAs.

Science.gov (United States)

Bellec, J; Delaby, N; Jouyaux, F; Perdrieux, M; Bouvier, J; Sorel, S; Henry, O; Lafond, C

2017-07-01

Robotic radiosurgery requires plan delivery quality assurance (DQA) but there has never been a published comprehensive analysis of a patient-specific DQA process in a clinic. We proposed to evaluate 350 consecutive film-based patient-specific DQAs using statistical process control. We evaluated the performance of the process to propose achievable tolerance criteria for DQA validation and we sought to identify suboptimal DQA using control charts. DQAs were performed on a CyberKnife-M6 using Gafchromic-EBT3 films. The signal-to-dose conversion was performed using a multichannel-correction and a scanning protocol that combined measurement and calibration in a single scan. The DQA analysis comprised a gamma-index analysis at 3%/1.5mm and a separate evaluation of spatial and dosimetric accuracy of the plan delivery. Each parameter was plotted on a control chart and control limits were calculated. A capability index (Cpm) was calculated to evaluate the ability of the process to produce results within specifications. The analysis of capability showed that a gamma pass rate of 85% at 3%/1.5mm was highly achievable as acceptance criteria for DQA validation using a film-based protocol (Cpm>1.33). 3.4% of DQA were outside a control limit of 88% for gamma pass-rate. The analysis of the out-of-control DQA helped identify a dosimetric error in our institute for a specific treatment type. We have defined initial tolerance criteria for DQA validations. We have shown that the implementation of a film-based patient-specific DQA protocol with the use of control charts is an effective method to improve patient treatment safety on CyberKnife. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Statistical analysis of hydrological response in urbanising catchments based on adaptive sampling using inter-amount times

Science.gov (United States)

ten Veldhuis, Marie-Claire; Schleiss, Marc

2017-04-01

Urban catchments are typically characterised by a more flashy nature of the hydrological response compared to natural catchments. Predicting flow changes associated with urbanisation is not straightforward, as they are influenced by interactions between impervious cover, basin size, drainage connectivity and stormwater management infrastructure. In this study, we present an alternative approach to statistical analysis of hydrological response variability and basin flashiness, based on the distribution of inter-amount times. We analyse inter-amount time distributions of high-resolution streamflow time series for 17 (semi-)urbanised basins in North Carolina, USA, ranging from 13 to 238 km2 in size. We show that in the inter-amount-time framework, sampling frequency is tuned to the local variability of the flow pattern, resulting in a different representation and weighting of high and low flow periods in the statistical distribution. This leads to important differences in the way the distribution quantiles, mean, coefficient of variation and skewness vary across scales and results in lower mean intermittency and improved scaling. Moreover, we show that inter-amount-time distributions can be used to detect regulation effects on flow patterns, identify critical sampling scales and characterise flashiness of hydrological response. The possibility to use both the classical approach and the inter-amount-time framework to identify minimum observable scales and analyse flow data opens up interesting areas for future research.

Statistical learning methods in high-energy and astrophysics analysis

Energy Technology Data Exchange (ETDEWEB)

Zimmermann, J. [Forschungszentrum Juelich GmbH, Zentrallabor fuer Elektronik, 52425 Juelich (Germany) and Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de; Kiesling, C. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)

2004-11-21

We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application.

Statistical learning methods in high-energy and astrophysics analysis

International Nuclear Information System (INIS)

Zimmermann, J.; Kiesling, C.

2004-01-01

We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application

Statistical Analysis Of Tank 19F Floor Sample Results

International Nuclear Information System (INIS)

Harris, S.

2010-01-01

Representative sampling has been completed for characterization of the residual material on the floor of Tank 19F as per the statistical sampling plan developed by Harris and Shine. Samples from eight locations have been obtained from the tank floor and two of the samples were archived as a contingency. Six samples, referred to in this report as the current scrape samples, have been submitted to and analyzed by SRNL. This report contains the statistical analysis of the floor sample analytical results to determine if further data are needed to reduce uncertainty. Included are comparisons with the prior Mantis samples results to determine if they can be pooled with the current scrape samples to estimate the upper 95% confidence limits (UCL95%) for concentration. Statistical analysis revealed that the Mantis and current scrape sample results are not compatible. Therefore, the Mantis sample results were not used to support the quantification of analytes in the residual material. Significant spatial variability among the current scrape sample results was not found. Constituent concentrations were similar between the North and South hemispheres as well as between the inner and outer regions of the tank floor. The current scrape sample results from all six samples fall within their 3-sigma limits. In view of the results from numerous statistical tests, the data were pooled from all six current scrape samples. As such, an adequate sample size was provided for quantification of the residual material on the floor of Tank 19F. The uncertainty is quantified in this report by an UCL95% on each analyte concentration. The uncertainty in analyte concentration was calculated as a function of the number of samples, the average, and the standard deviation of the analytical results. The UCL95% was based entirely on the six current scrape sample results (each averaged across three analytical determinations).

The fuzzy approach to statistical analysis

NARCIS (Netherlands)

Coppi, Renato; Gil, Maria A.; Kiers, Henk A. L.

2006-01-01

For the last decades, research studies have been developed in which a coalition of Fuzzy Sets Theory and Statistics has been established with different purposes. These namely are: (i) to introduce new data analysis problems in which the objective involves either fuzzy relationships or fuzzy terms;

Statistical analysis applied to safety culture self-assessment

International Nuclear Information System (INIS)

Macedo Soares, P.P.

2002-01-01

Interviews and opinion surveys are instruments used to assess the safety culture in an organization as part of the Safety Culture Enhancement Programme. Specific statistical tools are used to analyse the survey results. This paper presents an example of an opinion survey with the corresponding application of the statistical analysis and the conclusions obtained. Survey validation, Frequency statistics, Kolmogorov-Smirnov non-parametric test, Student (T-test) and ANOVA means comparison tests and LSD post-hoc multiple comparison test, are discussed. (author)

Foundation of statistical energy analysis in vibroacoustics

CERN Document Server

Le Bot, A

2015-01-01

This title deals with the statistical theory of sound and vibration. The foundation of statistical energy analysis is presented in great detail. In the modal approach, an introduction to random vibration with application to complex systems having a large number of modes is provided. For the wave approach, the phenomena of propagation, group speed, and energy transport are extensively discussed. Particular emphasis is given to the emergence of diffuse field, the central concept of the theory.

Methodology of Maqamat Hamadani and Hariri Based on Buseman’s statistical methodology

Directory of Open Access Journals (Sweden)

Hamed Sedghi

2016-02-01

Full Text Available Abstract  Stylistics can be defined as analysis and interpretation of the expression and different forms of speech, based on linguistic elements. German theorist , Buseman , devised his theses in statistical style based on the ratio of verbs to adjectives, generalizing a variety of literary and non- literary genera in German Language and Literature .   According to Buseman , increasing the number of verbs in verses, makes the text closer to literary style. Therefore, increasing the number of adjectives, makes the text closer to scientific or subjective style.   The most important achievement of statistical methodology of Buseman can be considered as: a comparison of author’s style, literary periods and genres; b studying the language system and variety of words; and, c classification and grading differences or similarities of literary, periods and genres.   The purpose of this study: Stylistic analysis of Maqamat Hamadani and al-Hariri based on the statistical model of Buseman.   The question proposed in this study : a How effective is the use of the statistical methods , in identifying and analyzing a variety of literature including Maqamat? b How effective is the quantitative scope of verbs and adjectives , as two key parameters in determining the style of literary works? And c Which element of fiction is most effective in enriching the literary approach ?   Specific research method is statistical – analysis ; We initially discovered and classified the number of verbs and adjectives in the Fifty- one of Hamadani Maqamehs and Fifty of Hariri Maqamehs ; then the scope of verbs and adjectives usage is shown in the form of tables and graphs . After that , we will assess the style of the literary work , based on the use of verbs and adjectives.   The research findings show : All Hamadani and Hariri Maqamat s quantitatively benefit from high -active approach, for the use of the verb . At 46 Maqameh Hamadani , and 48 Maqameh Hariri the

STATISTICS. The reusable holdout: Preserving validity in adaptive data analysis.

Science.gov (United States)

Dwork, Cynthia; Feldman, Vitaly; Hardt, Moritz; Pitassi, Toniann; Reingold, Omer; Roth, Aaron

2015-08-07

Misapplication of statistical data analysis is a common cause of spurious discoveries in scientific research. Existing approaches to ensuring the validity of inferences drawn from data assume a fixed procedure to be performed, selected before the data are examined. In common practice, however, data analysis is an intrinsically adaptive process, with new analyses generated on the basis of data exploration, as well as the results of previous analyses on the same data. We demonstrate a new approach for addressing the challenges of adaptivity based on insights from privacy-preserving data analysis. As an application, we show how to safely reuse a holdout data set many times to validate the results of adaptively chosen analyses. Copyright © 2015, American Association for the Advancement of Science.

Bayesian models based on test statistics for multiple hypothesis testing problems.

Science.gov (United States)

Ji, Yuan; Lu, Yiling; Mills, Gordon B

2008-04-01

We propose a Bayesian method for the problem of multiple hypothesis testing that is routinely encountered in bioinformatics research, such as the differential gene expression analysis. Our algorithm is based on modeling the distributions of test statistics under both null and alternative hypotheses. We substantially reduce the complexity of the process of defining posterior model probabilities by modeling the test statistics directly instead of modeling the full data. Computationally, we apply a Bayesian FDR approach to control the number of rejections of null hypotheses. To check if our model assumptions for the test statistics are valid for various bioinformatics experiments, we also propose a simple graphical model-assessment tool. Using extensive simulations, we demonstrate the performance of our models and the utility of the model-assessment tool. In the end, we apply the proposed methodology to an siRNA screening and a gene expression experiment.

Instruction of Statistics via Computer-Based Tools: Effects on Statistics' Anxiety, Attitude, and Achievement

Science.gov (United States)

Ciftci, S. Koza; Karadag, Engin; Akdal, Pinar

2014-01-01

The purpose of this study was to determine the effect of statistics instruction using computer-based tools, on statistics anxiety, attitude, and achievement. This study was designed as quasi-experimental research and the pattern used was a matched pre-test/post-test with control group design. Data was collected using three scales: a Statistics…

Statistical Analysis of Big Data on Pharmacogenomics

Science.gov (United States)

Fan, Jianqing; Liu, Han

2013-01-01

This paper discusses statistical methods for estimating complex correlation structure from large pharmacogenomic datasets. We selectively review several prominent statistical methods for estimating large covariance matrix for understanding correlation structure, inverse covariance matrix for network modeling, large-scale simultaneous tests for selecting significantly differently expressed genes and proteins and genetic markers for complex diseases, and high dimensional variable selection for identifying important molecules for understanding molecule mechanisms in pharmacogenomics. Their applications to gene network estimation and biomarker selection are used to illustrate the methodological power. Several new challenges of Big data analysis, including complex data distribution, missing data, measurement error, spurious correlation, endogeneity, and the need for robust statistical methods, are also discussed. PMID:23602905

HistFitter software framework for statistical data analysis

Energy Technology Data Exchange (ETDEWEB)

Baak, M. [CERN, Geneva (Switzerland); Besjes, G.J. [Radboud University Nijmegen, Nijmegen (Netherlands); Nikhef, Amsterdam (Netherlands); Cote, D. [University of Texas, Arlington (United States); Koutsman, A. [TRIUMF, Vancouver (Canada); Lorenz, J. [Ludwig-Maximilians-Universitaet Muenchen, Munich (Germany); Excellence Cluster Universe, Garching (Germany); Short, D. [University of Oxford, Oxford (United Kingdom)

2015-04-15

We present a software framework for statistical data analysis, called HistFitter, that has been used extensively by the ATLAS Collaboration to analyze big datasets originating from proton-proton collisions at the Large Hadron Collider at CERN. Since 2012 HistFitter has been the standard statistical tool in searches for supersymmetric particles performed by ATLAS. HistFitter is a programmable and flexible framework to build, book-keep, fit, interpret and present results of data models of nearly arbitrary complexity. Starting from an object-oriented configuration, defined by users, the framework builds probability density functions that are automatically fit to data and interpreted with statistical tests. Internally HistFitter uses the statistics packages RooStats and HistFactory. A key innovation of HistFitter is its design, which is rooted in analysis strategies of particle physics. The concepts of control, signal and validation regions are woven into its fabric. These are progressively treated with statistically rigorous built-in methods. Being capable of working with multiple models at once that describe the data, HistFitter introduces an additional level of abstraction that allows for easy bookkeeping, manipulation and testing of large collections of signal hypotheses. Finally, HistFitter provides a collection of tools to present results with publication quality style through a simple command-line interface. (orig.)

HistFitter software framework for statistical data analysis

International Nuclear Information System (INIS)

Baak, M.; Besjes, G.J.; Cote, D.; Koutsman, A.; Lorenz, J.; Short, D.

2015-01-01

We present a software framework for statistical data analysis, called HistFitter, that has been used extensively by the ATLAS Collaboration to analyze big datasets originating from proton-proton collisions at the Large Hadron Collider at CERN. Since 2012 HistFitter has been the standard statistical tool in searches for supersymmetric particles performed by ATLAS. HistFitter is a programmable and flexible framework to build, book-keep, fit, interpret and present results of data models of nearly arbitrary complexity. Starting from an object-oriented configuration, defined by users, the framework builds probability density functions that are automatically fit to data and interpreted with statistical tests. Internally HistFitter uses the statistics packages RooStats and HistFactory. A key innovation of HistFitter is its design, which is rooted in analysis strategies of particle physics. The concepts of control, signal and validation regions are woven into its fabric. These are progressively treated with statistically rigorous built-in methods. Being capable of working with multiple models at once that describe the data, HistFitter introduces an additional level of abstraction that allows for easy bookkeeping, manipulation and testing of large collections of signal hypotheses. Finally, HistFitter provides a collection of tools to present results with publication quality style through a simple command-line interface. (orig.)

STATISTICAL ANALYSIS OF DIESEL CAR REPAIRS ON THE EXAMPLE OF DIESEL SERVICE ADAMCZYK COMPANIES

Directory of Open Access Journals (Sweden)

Łukasz KONIECZNY

2014-12-01

Full Text Available The article presents a statistical analysis of car repair data gathered by an examined company over five-year time interval. It is based on a SQL database which contains information about all realized orders. The analysis defines the structure of the set of repaired car makes and additionally to find the most frequent vehicle defects.

Tract-based spatial statistics analysis of diffusion-tensor imaging data in pediatric- and adult-onset multiple sclerosis.

Science.gov (United States)

Aliotta, Rachel; Cox, Jennifer L; Donohue, Katelyn; Weinstock-Guttman, Bianca; Yeh, E Ann; Polak, Paul; Dwyer, Michael G; Zivadinov, Robert

2014-01-01

White matter (WM) microstructure may vary significantly in pediatric-onset (PO) and adult-onset (AO) patients with multiple sclerosis (MS), a difference that could be explained by the effects of an inherent plasticity in the affected pediatric brains early in the disease, and a phenomenon that does not occur later in life. This hypothesis would support the observation that disease progression is much slower in POMS compared to AOMS patients. To examine WM microstructure in the brain of adults with POMS and AOMS, using tract based spatial statistics (TBSS) analysis of diffusion-tensor imaging (DTI). Adults with relapsing-remitting (RR) POMS, who were diagnosed before age of 18 years (n = 16), were compared with age-matched (AOA, n = 23) and disease duration-matched (AOD, n = 22) RR patients who developed MS after the age of 18 years. Scans were analyzed using the FSL software package (Oxford, UK) and statistics were performed using TBSS to evaluate WM microstructure between groups based on the mean fractional anisotropy (FA) values obtained from the DTI. Widespread cortical and deep WM area differences characterized by increased FA values were seen in the AOAMS compared with POMS group (P < 0.05, TFCE corrected). Significantly increased FA values of posterior WM areas were detected in the AODMS compared with POMS group (P < 0.05, TFCE corrected). Increased FA values in WM areas of the AOMS compared with the POMS patients suggest that diffuse WM microstructure changes are more attributable to age of onset than a simple function of disease duration and age. Copyright © 2012 Wiley Periodicals, Inc.

Robust statistics and geochemical data analysis

International Nuclear Information System (INIS)

Di, Z.

1987-01-01

Advantages of robust procedures over ordinary least-squares procedures in geochemical data analysis is demonstrated using NURE data from the Hot Springs Quadrangle, South Dakota, USA. Robust principal components analysis with 5% multivariate trimming successfully guarded the analysis against perturbations by outliers and increased the number of interpretable factors. Regression with SINE estimates significantly increased the goodness-of-fit of the regression and improved the correspondence of delineated anomalies with known uranium prospects. Because of the ubiquitous existence of outliers in geochemical data, robust statistical procedures are suggested as routine procedures to replace ordinary least-squares procedures

Testing for Statistical Discrimination based on Gender

DEFF Research Database (Denmark)

Lesner, Rune Vammen

. It is shown that the implications of both screening discrimination and stereotyping are consistent with observable wage dynamics. In addition, it is found that the gender wage gap decreases in tenure but increases in job transitions and that the fraction of women in high-ranking positions within a firm does......This paper develops a model which incorporates the two most commonly cited strands of the literature on statistical discrimination, namely screening discrimination and stereotyping. The model is used to provide empirical evidence of statistical discrimination based on gender in the labour market...... not affect the level of statistical discrimination by gender....

Using Pre-Statistical Analysis to Streamline Monitoring Assessments

International Nuclear Information System (INIS)

Reed, J.K.

1999-01-01

A variety of statistical methods exist to aid evaluation of groundwater quality and subsequent decision making in regulatory programs. These methods are applied because of large temporal and spatial extrapolations commonly applied to these data. In short, statistical conclusions often serve as a surrogate for knowledge. However, facilities with mature monitoring programs that have generated abundant data have inherently less uncertainty because of the sheer quantity of analytical results. In these cases, statistical tests can be less important, and ''expert'' data analysis should assume an important screening role.The WSRC Environmental Protection Department, working with the General Separations Area BSRI Environmental Restoration project team has developed a method for an Integrated Hydrogeological Analysis (IHA) of historical water quality data from the F and H Seepage Basins groundwater remediation project. The IHA combines common sense analytical techniques and a GIS presentation that force direct interactive evaluation of the data. The IHA can perform multiple data analysis tasks required by the RCRA permit. These include: (1) Development of a groundwater quality baseline prior to remediation startup, (2) Targeting of constituents for removal from RCRA GWPS, (3) Targeting of constituents for removal from UIC, permit, (4) Targeting of constituents for reduced, (5)Targeting of monitoring wells not producing representative samples, (6) Reduction in statistical evaluation, and (7) Identification of contamination from other facilities

Statistical analysis of Thematic Mapper Simulator data for the geobotanical discrimination of rock types in southwest Oregon

Science.gov (United States)

Morrissey, L. A.; Weinstock, K. J.; Mouat, D. A.; Card, D. H.

1984-01-01

An evaluation of Thematic Mapper Simulator (TMS) data for the geobotanical discrimination of rock types based on vegetative cover characteristics is addressed in this research. A methodology for accomplishing this evaluation utilizing univariate and multivariate techniques is presented. TMS data acquired with a Daedalus DEI-1260 multispectral scanner were integrated with vegetation and geologic information for subsequent statistical analyses, which included a chi-square test, an analysis of variance, stepwise discriminant analysis, and Duncan's multiple range test. Results indicate that ultramafic rock types are spectrally separable from nonultramafics based on vegetative cover through the use of statistical analyses.

Statistical analysis of ultrasonic measurements in concrete

Science.gov (United States)

Chiang, Chih-Hung; Chen, Po-Chih

2002-05-01

Stress wave techniques such as measurements of ultrasonic pulse velocity are often used to evaluate concrete quality in structures. For proper interpretation of measurement results, the dependence of pulse transit time on the average acoustic impedance and the material homogeneity along the sound path need to be examined. Semi-direct measurement of pulse velocity could be more convenient than through transmission measurement. It is not necessary to assess both sides of concrete floors or walls. A novel measurement scheme is proposed and verified based on statistical analysis. It is shown that Semi-direct measurements are very effective for gathering large amount of pulse velocity data from concrete reference specimens. The variability of measurements is comparable with that reported by American Concrete Institute using either break-off or pullout tests.

Cost and quality effectiveness of objective-based and statistically-based quality control for volatile organic compounds analyses of gases

International Nuclear Information System (INIS)

Bennett, J.T.; Crowder, C.A.; Connolly, M.J.

1994-01-01

Gas samples from drums of radioactive waste at the Department of Energy (DOE) Idaho National Engineering Laboratory are being characterized for 29 volatile organic compounds to determine the feasibility of storing the waste in DOE's Waste Isolation Pilot Plant (WIPP) in Carlsbad, New Mexico. Quality requirements for the gas chromatography (GC) and GC/mass spectrometry chemical methods used to analyze the waste are specified in the Quality Assurance Program Plan for the WIPP Experimental Waste Characterization Program. Quality requirements consist of both objective criteria (data quality objectives, DQOs) and statistical criteria (process control). The DQOs apply to routine sample analyses, while the statistical criteria serve to determine and monitor precision and accuracy (P ampersand A) of the analysis methods and are also used to assign upper confidence limits to measurement results close to action levels. After over two years and more than 1000 sample analyses there are two general conclusions concerning the two approaches to quality control: (1) Objective criteria (e.g., ± 25% precision, ± 30% accuracy) based on customer needs and the usually prescribed criteria for similar EPA- approved methods are consistently attained during routine analyses. (2) Statistical criteria based on short term method performance are almost an order of magnitude more stringent than objective criteria and are difficult to satisfy following the same routine laboratory procedures which satisfy the objective criteria. A more cost effective and representative approach to establishing statistical method performances criteria would be either to utilize a moving average of P ampersand A from control samples over a several month time period or to determine within a sample variation by one-way analysis of variance of several months replicate sample analysis results or both. Confidence intervals for results near action levels could also be determined by replicate analysis of the sample in

SOCR: Statistics Online Computational Resource

OpenAIRE

Dinov, Ivo D.

2006-01-01

The need for hands-on computer laboratory experience in undergraduate and graduate statistics education has been firmly established in the past decade. As a result a number of attempts have been undertaken to develop novel approaches for problem-driven statistical thinking, data analysis and result interpretation. In this paper we describe an integrated educational web-based framework for: interactive distribution modeling, virtual online probability experimentation, statistical data analysis...

Statistical Model of Extreme Shear

DEFF Research Database (Denmark)

Larsen, Gunner Chr.; Hansen, Kurt Schaldemose

2004-01-01

In order to continue cost-optimisation of modern large wind turbines, it is important to continously increase the knowledge on wind field parameters relevant to design loads. This paper presents a general statistical model that offers site-specific prediction of the probability density function...... by a model that, on a statistically consistent basis, describe the most likely spatial shape of an extreme wind shear event. Predictions from the model have been compared with results from an extreme value data analysis, based on a large number of high-sampled full-scale time series measurements...... are consistent, given the inevitabel uncertainties associated with model as well as with the extreme value data analysis. Keywords: Statistical model, extreme wind conditions, statistical analysis, turbulence, wind loading, statistical analysis, turbulence, wind loading, wind shear, wind turbines....

Applied statistical training to strengthen analysis and health research capacity in Rwanda.

Science.gov (United States)

Thomson, Dana R; Semakula, Muhammed; Hirschhorn, Lisa R; Murray, Megan; Ndahindwa, Vedaste; Manzi, Anatole; Mukabutera, Assumpta; Karema, Corine; Condo, Jeanine; Hedt-Gauthier, Bethany

2016-09-29

To guide efficient investment of limited health resources in sub-Saharan Africa, local researchers need to be involved in, and guide, health system and policy research. While extensive survey and census data are available to health researchers and program officers in resource-limited countries, local involvement and leadership in research is limited due to inadequate experience, lack of dedicated research time and weak interagency connections, among other challenges. Many research-strengthening initiatives host prolonged fellowships out-of-country, yet their approaches have not been evaluated for effectiveness in involvement and development of local leadership in research. We developed, implemented and evaluated a multi-month, deliverable-driven, survey analysis training based in Rwanda to strengthen skills of five local research leaders, 15 statisticians, and a PhD candidate. Research leaders applied with a specific research question relevant to country challenges and committed to leading an analysis to publication. Statisticians with prerequisite statistical training and experience with a statistical software applied to participate in class-based trainings and complete an assigned analysis. Both statisticians and research leaders were provided ongoing in-country mentoring for analysis and manuscript writing. Participants reported a high level of skill, knowledge and collaborator development from class-based trainings and out-of-class mentorship that were sustained 1 year later. Five of six manuscripts were authored by multi-institution teams and submitted to international peer-reviewed scientific journals, and three-quarters of the participants mentored others in survey data analysis or conducted an additional survey analysis in the year following the training. Our model was effective in utilizing existing survey data and strengthening skills among full-time working professionals without disrupting ongoing work commitments and using few resources. Critical to our

ZnO crystals obtained by electrodeposition: Statistical analysis of most important process variables

International Nuclear Information System (INIS)

Cembrero, Jesus; Busquets-Mataix, David

2009-01-01

In this paper a comparative study by means of a statistical analysis of the main process variables affecting ZnO crystal electrodeposition is presented. ZnO crystals were deposited on two different substrates, silicon wafer and indium tin oxide. The control variables were substrate types, electrolyte concentration, temperature, exposition time and current density. The morphologies of the different substrates were observed using scanning electron microscopy. The percentage of substrate area covered by ZnO deposit was calculated by computational image analysis. The design of the applied experiments was based on a two-level factorial analysis involving a series of 32 experiments and an analysis of variance. Statistical results reveal that variables exerting a significant influence on the area covered by ZnO deposit are electrolyte concentration, substrate type and time of deposition, together with a combined two-factor interaction between temperature and current density. However, morphology is also influenced by surface roughness of the substrates

Obtaining Internet Flow Statistics by Volunteer-Based System

DEFF Research Database (Denmark)

Pedersen, Jens Myrup; Bujlow, Tomasz

2012-01-01

In this paper we demonstrate how the Volunteer Based System for Research on the Internet, developed at Aalborg University, can be used for creating statistics of Internet usage. Since the data is collected on individual machines, the statistics can be made on the basis of both individual users......, and average flow durations. The paper is concluded with a discussion on what further statistics can be made, and the further development of the system....

Statistical models based on conditional probability distributions

International Nuclear Information System (INIS)

Narayanan, R.S.

1991-10-01

We present a formulation of statistical mechanics models based on conditional probability distribution rather than a Hamiltonian. We show that it is possible to realize critical phenomena through this procedure. Closely linked with this formulation is a Monte Carlo algorithm, in which a configuration generated is guaranteed to be statistically independent from any other configuration for all values of the parameters, in particular near the critical point. (orig.)

Data analysis for radiological characterisation: Geostatistical and statistical complementarity

International Nuclear Information System (INIS)

Desnoyers, Yvon; Dubot, Didier

2012-01-01

Radiological characterisation may cover a large range of evaluation objectives during a decommissioning and dismantling (D and D) project: removal of doubt, delineation of contaminated materials, monitoring of the decontamination work and final survey. At each stage, collecting relevant data to be able to draw the conclusions needed is quite a big challenge. In particular two radiological characterisation stages require an advanced sampling process and data analysis, namely the initial categorization and optimisation of the materials to be removed and the final survey to demonstrate compliance with clearance levels. On the one hand the latter is widely used and well developed in national guides and norms, using random sampling designs and statistical data analysis. On the other hand a more complex evaluation methodology has to be implemented for the initial radiological characterisation, both for sampling design and for data analysis. The geostatistical framework is an efficient way to satisfy the radiological characterisation requirements providing a sound decision-making approach for the decommissioning and dismantling of nuclear premises. The relevance of the geostatistical methodology relies on the presence of a spatial continuity for radiological contamination. Thus geo-statistics provides reliable methods for activity estimation, uncertainty quantification and risk analysis, leading to a sound classification of radiological waste (surfaces and volumes). This way, the radiological characterization of contaminated premises can be divided into three steps. First, the most exhaustive facility analysis provides historical and qualitative information. Then, a systematic (exhaustive or not) surface survey of the contamination is implemented on a regular grid. Finally, in order to assess activity levels and contamination depths, destructive samples are collected at several locations within the premises (based on the surface survey results) and analysed. Combined with

Statistical analysis of magnetically soft particles in magnetorheological elastomers

Science.gov (United States)

Gundermann, T.; Cremer, P.; Löwen, H.; Menzel, A. M.; Odenbach, S.

2017-04-01

The physical properties of magnetorheological elastomers (MRE) are a complex issue and can be influenced and controlled in many ways, e.g. by applying a magnetic field, by external mechanical stimuli, or by an electric potential. In general, the response of MRE materials to these stimuli is crucially dependent on the distribution of the magnetic particles inside the elastomer. Specific knowledge of the interactions between particles or particle clusters is of high relevance for understanding the macroscopic rheological properties and provides an important input for theoretical calculations. In order to gain a better insight into the correlation between the macroscopic effects and microstructure and to generate a database for theoretical analysis, x-ray micro-computed tomography (X-μCT) investigations as a base for a statistical analysis of the particle configurations were carried out. Different MREs with quantities of 2-15 wt% (0.27-2.3 vol%) of iron powder and different allocations of the particles inside the matrix were prepared. The X-μCT results were edited by an image processing software regarding the geometrical properties of the particles with and without the influence of an external magnetic field. Pair correlation functions for the positions of the particles inside the elastomer were calculated to statistically characterize the distributions of the particles in the samples.

Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments

NARCIS (Netherlands)

Kleijnen, J.P.C.

1995-01-01

This tutorial discusses what-if analysis and optimization of System Dynamics models. These problems are solved, using the statistical techniques of regression analysis and design of experiments (DOE). These issues are illustrated by applying the statistical techniques to a System Dynamics model for

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

Czech Academy of Sciences Publication Activity Database

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

2002-01-01

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

Statistical analysis of the hydrodynamic pressure in the near field of compressible jets

International Nuclear Information System (INIS)

Camussi, R.; Di Marco, A.; Castelain, T.

2017-01-01

Highlights: • Statistical properties of pressure fluctuations retrieved through wavelet analysis • Time delay PDFs approximated by a log-normal distribution • Amplitude PDFs approximated by a Gamma distribution • Random variable PDFs weakly dependent upon position and Mach number. • A general stochastic model achieved for the distance dependency - Abstract: This paper is devoted to the statistical characterization of the pressure fluctuations measured in the near field of a compressible jet at two subsonic Mach numbers, 0.6 and 0.9. The analysis is focused on the hydrodynamic pressure measured at different distances from the jet exit and analyzed at the typical frequency associated to the Kelvin–Helmholtz instability. Statistical properties are retrieved by the application of the wavelet transform to the experimental data and the computation of the wavelet scalogram around that frequency. This procedure highlights traces of events that appear intermittently in time and have variable strength. A wavelet-based event tracking procedure has been applied providing a statistical characterization of the time delay between successive events and of their energy level. On this basis, two stochastic models are proposed and validated against the experimental data in the different flow conditions

Statistical analysis and interpretation of prenatal diagnostic imaging studies, Part 2: descriptive and inferential statistical methods.

Science.gov (United States)

Tuuli, Methodius G; Odibo, Anthony O

2011-08-01

The objective of this article is to discuss the rationale for common statistical tests used for the analysis and interpretation of prenatal diagnostic imaging studies. Examples from the literature are used to illustrate descriptive and inferential statistics. The uses and limitations of linear and logistic regression analyses are discussed in detail.

Performance Analysis of Millimeter-Wave Multi-hop Machine-to-Machine Networks Based on Hop Distance Statistics

Directory of Open Access Journals (Sweden)

Haejoon Jung

2018-01-01

Full Text Available As an intrinsic part of the Internet of Things (IoT ecosystem, machine-to-machine (M2M communications are expected to provide ubiquitous connectivity between machines. Millimeter-wave (mmWave communication is another promising technology for the future communication systems to alleviate the pressure of scarce spectrum resources. For this reason, in this paper, we consider multi-hop M2M communications, where a machine-type communication (MTC device with the limited transmit power relays to help other devices using mmWave. To be specific, we focus on hop distance statistics and their impacts on system performances in multi-hop wireless networks (MWNs with directional antenna arrays in mmWave for M2M communications. Different from microwave systems, in mmWave communications, wireless channel suffers from blockage by obstacles that heavily attenuate line-of-sight signals, which may result in limited per-hop progress in MWNs. We consider two routing strategies aiming at different types of applications and derive the probability distributions of their hop distances. Moreover, we provide their baseline statistics assuming the blockage-free scenario to quantify the impact of blockages. Based on the hop distance analysis, we propose a method to estimate the end-to-end performances (e.g., outage probability, hop count, and transmit energy of the mmWave MWNs, which provides important insights into mmWave MWN design without time-consuming and repetitive end-to-end simulation.

Performance Analysis of Millimeter-Wave Multi-hop Machine-to-Machine Networks Based on Hop Distance Statistics.

Science.gov (United States)

Jung, Haejoon; Lee, In-Ho

2018-01-12

As an intrinsic part of the Internet of Things (IoT) ecosystem, machine-to-machine (M2M) communications are expected to provide ubiquitous connectivity between machines. Millimeter-wave (mmWave) communication is another promising technology for the future communication systems to alleviate the pressure of scarce spectrum resources. For this reason, in this paper, we consider multi-hop M2M communications, where a machine-type communication (MTC) device with the limited transmit power relays to help other devices using mmWave. To be specific, we focus on hop distance statistics and their impacts on system performances in multi-hop wireless networks (MWNs) with directional antenna arrays in mmWave for M2M communications. Different from microwave systems, in mmWave communications, wireless channel suffers from blockage by obstacles that heavily attenuate line-of-sight signals, which may result in limited per-hop progress in MWNs. We consider two routing strategies aiming at different types of applications and derive the probability distributions of their hop distances. Moreover, we provide their baseline statistics assuming the blockage-free scenario to quantify the impact of blockages. Based on the hop distance analysis, we propose a method to estimate the end-to-end performances (e.g., outage probability, hop count, and transmit energy) of the mmWave MWNs, which provides important insights into mmWave MWN design without time-consuming and repetitive end-to-end simulation.

Multivariate statistical modelling based on generalized linear models

CERN Document Server

Fahrmeir, Ludwig

1994-01-01

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

Statistical analysis of environmental data

International Nuclear Information System (INIS)

Beauchamp, J.J.; Bowman, K.O.; Miller, F.L. Jr.

1975-10-01

This report summarizes the analyses of data obtained by the Radiological Hygiene Branch of the Tennessee Valley Authority from samples taken around the Browns Ferry Nuclear Plant located in Northern Alabama. The data collection was begun in 1968 and a wide variety of types of samples have been gathered on a regular basis. The statistical analysis of environmental data involving very low-levels of radioactivity is discussed. Applications of computer calculations for data processing are described

Testing for Statistical Discrimination based on Gender

OpenAIRE

Lesner, Rune Vammen

2016-01-01

This paper develops a model which incorporates the two most commonly cited strands of the literature on statistical discrimination, namely screening discrimination and stereotyping. The model is used to provide empirical evidence of statistical discrimination based on gender in the labour market. It is shown that the implications of both screening discrimination and stereotyping are consistent with observable wage dynamics. In addition, it is found that the gender wage gap decreases in tenure...

Highly Robust Statistical Methods in Medical Image Analysis

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2012-01-01

Roč. 32, č. 2 (2012), s. 3-16 ISSN 0208-5216 R&D Projects: GA MŠk(CZ) 1M06014 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust statistics * classification * faces * robust image analysis * forensic science Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.208, year: 2012 http://www.ibib.waw.pl/bbe/bbefulltext/BBE_32_2_003_FT.pdf

MR diffusion tensor analysis of schizophrenic brain using statistical parametric mapping

International Nuclear Information System (INIS)

Yamada, Haruyasu; Abe, Osamu; Kasai, Kiyoto

2005-01-01

The purpose of this study is to investigate diffusion anisotropy in the schizophrenic brain by voxel-based analysis of diffusion tensor imaging (DTI), using statistical parametric mapping (SPM). We studied 33 patients with schizophrenia diagnosed by diagnostic and statistical manual of mental disorders (DSM)-IV criteria and 42 matched controls. The data was obtained with a 1.5 T MRI system. We used single-shot spin-echo planar sequences (repetition time/echo time (TR/TE)=5000/102 ms, 5 mm slice thickness and 1.5 mm gap, field of view (FOV)=21 x 21 cm 2 , number of excitation (NEX)=4, 128 x 128 pixel matrix) for diffusion tensor acquisition. Diffusion gradients (b-value of 500 or 1000 s/mm 2 ) were applied on two axes simultaneously. Diffusion properties were measured along 6 non-linear directions. The structural distortion induced by the large diffusion gradients was corrected, based on each T 2 -weighted echo-planar image (b=0 s/mm 2 ). The fractional anisotropy (FA) maps were generated on a voxel-by-voxel basis. T 2 -weighted echo-planar images were then segmented into gray matter, white matter, and cerebrospinal fluid, using SPM (Wellcome Department of Imaging, University College London, UK). All apparent diffusion coefficient (ADC) and FA maps in native space were transformed to the stereotactic space by registering each of the images to the same template image. The normalized data was smoothed and analyzed using SPM. The significant FA decrease in the patient group was found in the uncinate fasciculus, parahippocampal white matter, anterior cingulum and other areas (corrected p<0.05). No significant increased region was noted. Our results may reflect reduced diffusion anisotropy of the white matter pathway of the limbic system as shown by the decreased FA. Manual region-of-interest analysis is usually more sensitive than voxel-based analysis, but it is subjective and difficult to set with anatomical reproducibility. Voxel-based analysis of the diffusion tensor

Advanced statistical methods in data science

CERN Document Server

Chen, Jiahua; Lu, Xuewen; Yi, Grace; Yu, Hao

2016-01-01

This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a fu...

Statistical Power Analysis with Missing Data A Structural Equation Modeling Approach

CERN Document Server

Davey, Adam

2009-01-01

Statistical power analysis has revolutionized the ways in which we conduct and evaluate research.  Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling.  It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types

Statistical Analysis of Data for Timber Strengths

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard

2003-01-01

Statistical analyses are performed for material strength parameters from a large number of specimens of structural timber. Non-parametric statistical analysis and fits have been investigated for the following distribution types: Normal, Lognormal, 2 parameter Weibull and 3-parameter Weibull...... fits to the data available, especially if tail fits are used whereas the Log Normal distribution generally gives a poor fit and larger coefficients of variation, especially if tail fits are used. The implications on the reliability level of typical structural elements and on partial safety factors...... for timber are investigated....

The role of ensemble-based statistics in variational assimilation of cloud-affected observations from infrared imagers

Science.gov (United States)

Hacker, Joshua; Vandenberghe, Francois; Jung, Byoung-Jo; Snyder, Chris

2017-04-01

Effective assimilation of cloud-affected radiance observations from space-borne imagers, with the aim of improving cloud analysis and forecasting, has proven to be difficult. Large observation biases, nonlinear observation operators, and non-Gaussian innovation statistics present many challenges. Ensemble-variational data assimilation (EnVar) systems offer the benefits of flow-dependent background error statistics from an ensemble, and the ability of variational minimization to handle nonlinearity. The specific benefits of ensemble statistics, relative to static background errors more commonly used in variational systems, have not been quantified for the problem of assimilating cloudy radiances. A simple experiment framework is constructed with a regional NWP model and operational variational data assimilation system, to provide the basis understanding the importance of ensemble statistics in cloudy radiance assimilation. Restricting the observations to those corresponding to clouds in the background forecast leads to innovations that are more Gaussian. The number of large innovations is reduced compared to the more general case of all observations, but not eliminated. The Huber norm is investigated to handle the fat tails of the distributions, and allow more observations to be assimilated without the need for strict background checks that eliminate them. Comparing assimilation using only ensemble background error statistics with assimilation using only static background error statistics elucidates the importance of the ensemble statistics. Although the cost functions in both experiments converge to similar values after sufficient outer-loop iterations, the resulting cloud water, ice, and snow content are greater in the ensemble-based analysis. The subsequent forecasts from the ensemble-based analysis also retain more condensed water species, indicating that the local environment is more supportive of clouds. In this presentation we provide details that explain the

Numeric computation and statistical data analysis on the Java platform

CERN Document Server

Chekanov, Sergei V

2016-01-01

Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language. The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries. The code snippets are so short that they easily fit into single pages. Numeric Computation and Statistical Data Analysis ...

A Divergence Statistics Extension to VTK for Performance Analysis

Energy Technology Data Exchange (ETDEWEB)

Pebay, Philippe Pierre [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bennett, Janine Camille [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-02-01

This report follows the series of previous documents ([PT08, BPRT09b, PT09, BPT09, PT10, PB13], where we presented the parallel descriptive, correlative, multi-correlative, principal component analysis, contingency, k -means, order and auto-correlative statistics engines which we developed within the Visualization Tool Kit ( VTK ) as a scalable, parallel and versatile statistics package. We now report on a new engine which we developed for the calculation of divergence statistics, a concept which we hereafter explain and whose main goal is to quantify the discrepancy, in a stasticial manner akin to measuring a distance, between an observed empirical distribution and a theoretical, "ideal" one. The ease of use of the new diverence statistics engine is illustrated by the means of C++ code snippets. Although this new engine does not yet have a parallel implementation, it has already been applied to HPC performance analysis, of which we provide an example.

Time-Dependent Statistical Analysis of Wide-Area Time-Synchronized Data

Directory of Open Access Journals (Sweden)

A. R. Messina

2010-01-01

Full Text Available Characterization of spatial and temporal changes in the dynamic patterns of a nonstationary process is a problem of great theoretical and practical importance. On-line monitoring of large-scale power systems by means of time-synchronized Phasor Measurement Units (PMUs provides the opportunity to analyze and characterize inter-system oscillations. Wide-area measurement sets, however, are often relatively large, and may contain phenomena with differing temporal scales. Extracting from these measurements the relevant dynamics is a difficult problem. As the number of observations of real events continues to increase, statistical techniques are needed to help identify relevant temporal dynamics from noise or random effects in measured data. In this paper, a statistically based, data-driven framework that integrates the use of wavelet-based EOF analysis and a sliding window-based method is proposed to identify and extract, in near-real-time, dynamically independent spatiotemporal patterns from time synchronized data. The method deals with the information in space and time simultaneously, and allows direct tracking and characterization of the nonstationary time-frequency dynamics of oscillatory processes. The efficiency and accuracy of the developed procedures for extracting localized information of power system behavior from time-synchronized phasor measurements of a real event in Mexico is assessed.

An approach to build knowledge base for reactor accident diagnostic system using statistical method

International Nuclear Information System (INIS)

Kohsaka, Atsuo; Yokobayashi, Masao; Matsumoto, Kiyoshi; Fujii, Minoru

1988-01-01

In the development of a rule based expert system, one of key issues is how to build a knowledge base (KB). A systematic approach has been attempted for building an objective KB efficiently. The approach is based on the concept that a prototype KB should first be generated in a systematic way and then it is to be modified and/or improved by expert for practical use. The statistical method, Factor Analysis, was applied to build a prototype KB for the JAERI expert system DISKET using source information obtained from a PWR simulator. The prototype KB was obtained and the inference with this KB was performed against several types of transients. In each diagnosis, the transient type was well identified. From this study, it is concluded that the statistical method used is useful for building a prototype knowledge base. (author)

Developments in statistical analysis in quantitative genetics

DEFF Research Database (Denmark)

Sorensen, Daniel

2009-01-01

of genetic means and variances, models for the analysis of categorical and count data, the statistical genetics of a model postulating that environmental variance is partly under genetic control, and a short discussion of models that incorporate massive genetic marker information. We provide an overview......A remarkable research impetus has taken place in statistical genetics since the last World Conference. This has been stimulated by breakthroughs in molecular genetics, automated data-recording devices and computer-intensive statistical methods. The latter were revolutionized by the bootstrap...... and by Markov chain Monte Carlo (McMC). In this overview a number of specific areas are chosen to illustrate the enormous flexibility that McMC has provided for fitting models and exploring features of data that were previously inaccessible. The selected areas are inferences of the trajectories over time...

On the Statistical Validation of Technical Analysis

Directory of Open Access Journals (Sweden)

Rosane Riera Freire

2007-06-01

Full Text Available Technical analysis, or charting, aims on visually identifying geometrical patterns in price charts in order to antecipate price "trends". In this paper we revisit the issue of thecnical analysis validation which has been tackled in the literature without taking care for (i the presence of heterogeneity and (ii statistical dependence in the analyzed data - various agglutinated return time series from distinct financial securities. The main purpose here is to address the first cited problem by suggesting a validation methodology that also "homogenizes" the securities according to the finite dimensional probability distribution of their return series. The general steps go through the identification of the stochastic processes for the securities returns, the clustering of similar securities and, finally, the identification of presence, or absence, of informatinal content obtained from those price patterns. We illustrate the proposed methodology with a real data exercise including several securities of the global market. Our investigation shows that there is a statistically significant informational content in two out of three common patterns usually found through technical analysis, namely: triangle, rectangle and head and shoulders.

Statistical testing and power analysis for brain-wide association study.

Science.gov (United States)

Gong, Weikang; Wan, Lin; Lu, Wenlian; Ma, Liang; Cheng, Fan; Cheng, Wei; Grünewald, Stefan; Feng, Jianfeng

2018-04-05

The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression, the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method bypasses the need of non-parametric permutation to correct for multiple comparison, thus, it can efficiently tackle large datasets with high resolution fMRI images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods fail. A software package is available at https://github.com/weikanggong/BWAS. Copyright © 2018 Elsevier B.V. All rights reserved.

Statistical Shape Analysis of the Human Ear Canal with Application to In-the-Ear Hearing Aid Design

DEFF Research Database (Denmark)

Paulsen, Rasmus Reinhold

2004-01-01

This thesis is about the statistical shape analysis of the human ear canal with application to the mechanical design of in-the-ear hearing aids. Initially, it is described how a statistical shape model of the human ear canal is built based on a training set of laser-scanned ear impressions. A thin...

Data management and statistical analysis for environmental assessment

International Nuclear Information System (INIS)

Wendelberger, J.R.; McVittie, T.I.

1995-01-01

Data management and statistical analysis for environmental assessment are important issues on the interface of computer science and statistics. Data collection for environmental decision making can generate large quantities of various types of data. A database/GIS system developed is described which provides efficient data storage as well as visualization tools which may be integrated into the data analysis process. FIMAD is a living database and GIS system. The system has changed and developed over time to meet the needs of the Los Alamos National Laboratory Restoration Program. The system provides a repository for data which may be accessed by different individuals for different purposes. The database structure is driven by the large amount and varied types of data required for environmental assessment. The integration of the database with the GIS system provides the foundation for powerful visualization and analysis capabilities

STATISTICAL ANALYSIS OF TANK 18F FLOOR SAMPLE RESULTS

Energy Technology Data Exchange (ETDEWEB)

Harris, S.

2010-09-02

Representative sampling has been completed for characterization of the residual material on the floor of Tank 18F as per the statistical sampling plan developed by Shine [1]. Samples from eight locations have been obtained from the tank floor and two of the samples were archived as a contingency. Six samples, referred to in this report as the current scrape samples, have been submitted to and analyzed by SRNL [2]. This report contains the statistical analysis of the floor sample analytical results to determine if further data are needed to reduce uncertainty. Included are comparisons with the prior Mantis samples results [3] to determine if they can be pooled with the current scrape samples to estimate the upper 95% confidence limits (UCL{sub 95%}) for concentration. Statistical analysis revealed that the Mantis and current scrape sample results are not compatible. Therefore, the Mantis sample results were not used to support the quantification of analytes in the residual material. Significant spatial variability among the current sample results was not found. Constituent concentrations were similar between the North and South hemispheres as well as between the inner and outer regions of the tank floor. The current scrape sample results from all six samples fall within their 3-sigma limits. In view of the results from numerous statistical tests, the data were pooled from all six current scrape samples. As such, an adequate sample size was provided for quantification of the residual material on the floor of Tank 18F. The uncertainty is quantified in this report by an upper 95% confidence limit (UCL{sub 95%}) on each analyte concentration. The uncertainty in analyte concentration was calculated as a function of the number of samples, the average, and the standard deviation of the analytical results. The UCL{sub 95%} was based entirely on the six current scrape sample results (each averaged across three analytical determinations).

Statistical analysis and application of quasi experiments to antimicrobial resistance intervention studies.

Science.gov (United States)

Shardell, Michelle; Harris, Anthony D; El-Kamary, Samer S; Furuno, Jon P; Miller, Ram R; Perencevich, Eli N

2007-10-01

Quasi-experimental study designs are frequently used to assess interventions that aim to limit the emergence of antimicrobial-resistant pathogens. However, previous studies using these designs have often used suboptimal statistical methods, which may result in researchers making spurious conclusions. Methods used to analyze quasi-experimental data include 2-group tests, regression analysis, and time-series analysis, and they all have specific assumptions, data requirements, strengths, and limitations. An example of a hospital-based intervention to reduce methicillin-resistant Staphylococcus aureus infection rates and reduce overall length of stay is used to explore these methods.

Simulation on a car interior aerodynamic noise control based on statistical energy analysis

Science.gov (United States)

Chen, Xin; Wang, Dengfeng; Ma, Zhengdong

2012-09-01

How to simulate interior aerodynamic noise accurately is an important question of a car interior noise reduction. The unsteady aerodynamic pressure on body surfaces is proved to be the key effect factor of car interior aerodynamic noise control in high frequency on high speed. In this paper, a detail statistical energy analysis (SEA) model is built. And the vibra-acoustic power inputs are loaded on the model for the valid result of car interior noise analysis. The model is the solid foundation for further optimization on car interior noise control. After the most sensitive subsystems for the power contribution to car interior noise are pointed by SEA comprehensive analysis, the sound pressure level of car interior aerodynamic noise can be reduced by improving their sound and damping characteristics. The further vehicle testing results show that it is available to improve the interior acoustic performance by using detailed SEA model, which comprised by more than 80 subsystems, with the unsteady aerodynamic pressure calculation on body surfaces and the materials improvement of sound/damping properties. It is able to acquire more than 2 dB reduction on the central frequency in the spectrum over 800 Hz. The proposed optimization method can be looked as a reference of car interior aerodynamic noise control by the detail SEA model integrated unsteady computational fluid dynamics (CFD) and sensitivity analysis of acoustic contribution.

A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

Science.gov (United States)

Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

2015-09-01

Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes

Science.gov (United States)

Müller, M. F.; Thompson, S. E.

2016-02-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drivers of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by frequent wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are favored over statistical models.

Diagnosis checking of statistical analysis in RCTs indexed in PubMed.

Science.gov (United States)

Lee, Paul H; Tse, Andy C Y

2017-11-01

Statistical analysis is essential for reporting of the results of randomized controlled trials (RCTs), as well as evaluating their effectiveness. However, the validity of a statistical analysis also depends on whether the assumptions of that analysis are valid. To review all RCTs published in journals indexed in PubMed during December 2014 to provide a complete picture of how RCTs handle assumptions of statistical analysis. We reviewed all RCTs published in December 2014 that appeared in journals indexed in PubMed using the Cochrane highly sensitive search strategy. The 2014 impact factors of the journals were used as proxies for their quality. The type of statistical analysis used and whether the assumptions of the analysis were tested were reviewed. In total, 451 papers were included. Of the 278 papers that reported a crude analysis for the primary outcomes, 31 (27·2%) reported whether the outcome was normally distributed. Of the 172 papers that reported an adjusted analysis for the primary outcomes, diagnosis checking was rarely conducted, with only 20%, 8·6% and 7% checked for generalized linear model, Cox proportional hazard model and multilevel model, respectively. Study characteristics (study type, drug trial, funding sources, journal type and endorsement of CONSORT guidelines) were not associated with the reporting of diagnosis checking. The diagnosis of statistical analyses in RCTs published in PubMed-indexed journals was usually absent. Journals should provide guidelines about the reporting of a diagnosis of assumptions. © 2017 Stichting European Society for Clinical Investigation Journal Foundation.

Building the Community Online Resource for Statistical Seismicity Analysis (CORSSA)

Science.gov (United States)

Michael, A. J.; Wiemer, S.; Zechar, J. D.; Hardebeck, J. L.; Naylor, M.; Zhuang, J.; Steacy, S.; Corssa Executive Committee

2010-12-01

Statistical seismology is critical to the understanding of seismicity, the testing of proposed earthquake prediction and forecasting methods, and the assessment of seismic hazard. Unfortunately, despite its importance to seismology - especially to those aspects with great impact on public policy - statistical seismology is mostly ignored in the education of seismologists, and there is no central repository for the existing open-source software tools. To remedy these deficiencies, and with the broader goal to enhance the quality of statistical seismology research, we have begun building the Community Online Resource for Statistical Seismicity Analysis (CORSSA). CORSSA is a web-based educational platform that is authoritative, up-to-date, prominent, and user-friendly. We anticipate that the users of CORSSA will range from beginning graduate students to experienced researchers. More than 20 scientists from around the world met for a week in Zurich in May 2010 to kick-start the creation of CORSSA: the format and initial table of contents were defined; a governing structure was organized; and workshop participants began drafting articles. CORSSA materials are organized with respect to six themes, each containing between four and eight articles. The CORSSA web page, www.corssa.org, officially unveiled on September 6, 2010, debuts with an initial set of approximately 10 to 15 articles available online for viewing and commenting with additional articles to be added over the coming months. Each article will be peer-reviewed and will present a balanced discussion, including illustrative examples and code snippets. Topics in the initial set of articles will include: introductions to both CORSSA and statistical seismology, basic statistical tests and their role in seismology; understanding seismicity catalogs and their problems; basic techniques for modeling seismicity; and methods for testing earthquake predictability hypotheses. A special article will compare and review

Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

Science.gov (United States)

Kim, D.; Youn, J.; Kim, C.

2017-08-01

As a malfunctioning PV (Photovoltaic) cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

AUTOMATIC FAULT RECOGNITION OF PHOTOVOLTAIC MODULES BASED ON STATISTICAL ANALYSIS OF UAV THERMOGRAPHY

Directory of Open Access Journals (Sweden)

D. Kim

2017-08-01

Full Text Available As a malfunctioning PV (Photovoltaic cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle. The proposed algorithm uses statistical analysis of thermal intensity (surface temperature characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

[Delirium in stroke patients : Critical analysis of statistical procedures for the identification of risk factors].

Science.gov (United States)

Nydahl, P; Margraf, N G; Ewers, A

2017-04-01

Delirium is a relevant complication following an acute stroke. It is a multifactor occurrence with numerous interacting risk factors that alternately influence each other. The risk factors of delirium in stroke patients are often based on limited clinical studies. The statistical procedures and clinical relevance of delirium related risk factors in adult stroke patients should therefore be questioned. This secondary analysis includes clinically relevant studies that give evidence for the clinical relevance and statistical significance of delirium-associated risk factors in stroke patients. The quality of the reporting of regression analyses was assessed using Ottenbacher's quality criteria. The delirium-associated risk factors identified were examined with regard to statistical significance using the Bonferroni method of multiple testing for forming incorrect positive hypotheses. This was followed by a literature-based discussion on clinical relevance. Nine clinical studies were included. None of the studies fulfilled all the prerequisites and assumptions given for the reporting of regression analyses according to Ottenbacher. Of the 108 delirium-associated risk factors, a total of 48 (44.4%) were significant, whereby a total of 28 (58.3%) were false positive after Bonferroni correction. Following a literature-based discussion on clinical relevance, the assumption of statistical significance and clinical relevance could be found for only four risk factors (dementia or cognitive impairment, total anterior infarct, severe infarct and infections). The statistical procedures used in the existing literature are questionable, as are their results. A post-hoc analysis and critical appraisal reduced the number of possible delirium-associated risk factors to just a few clinically relevant factors.

A κ-generalized statistical mechanics approach to income analysis

Science.gov (United States)

Clementi, F.; Gallegati, M.; Kaniadakis, G.

2009-02-01

This paper proposes a statistical mechanics approach to the analysis of income distribution and inequality. A new distribution function, having its roots in the framework of κ-generalized statistics, is derived that is particularly suitable for describing the whole spectrum of incomes, from the low-middle income region up to the high income Pareto power-law regime. Analytical expressions for the shape, moments and some other basic statistical properties are given. Furthermore, several well-known econometric tools for measuring inequality, which all exist in a closed form, are considered. A method for parameter estimation is also discussed. The model is shown to fit remarkably well the data on personal income for the United States, and the analysis of inequality performed in terms of its parameters is revealed as very powerful.

A κ-generalized statistical mechanics approach to income analysis

International Nuclear Information System (INIS)

Clementi, F; Gallegati, M; Kaniadakis, G

2009-01-01

This paper proposes a statistical mechanics approach to the analysis of income distribution and inequality. A new distribution function, having its roots in the framework of κ-generalized statistics, is derived that is particularly suitable for describing the whole spectrum of incomes, from the low–middle income region up to the high income Pareto power-law regime. Analytical expressions for the shape, moments and some other basic statistical properties are given. Furthermore, several well-known econometric tools for measuring inequality, which all exist in a closed form, are considered. A method for parameter estimation is also discussed. The model is shown to fit remarkably well the data on personal income for the United States, and the analysis of inequality performed in terms of its parameters is revealed as very powerful

Damage detection of engine bladed-disks using multivariate statistical analysis

Science.gov (United States)

Fang, X.; Tang, J.

2006-03-01

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

Normality Tests for Statistical Analysis: A Guide for Non-Statisticians

Science.gov (United States)

Ghasemi, Asghar; Zahediasl, Saleh

2012-01-01

Statistical errors are common in scientific literature and about 50% of the published articles have at least one error. The assumption of normality needs to be checked for many statistical procedures, namely parametric tests, because their validity depends on it. The aim of this commentary is to overview checking for normality in statistical analysis using SPSS. PMID:23843808

Statistics for business

CERN Document Server

Waller, Derek L

2008-01-01

Statistical analysis is essential to business decision-making and management, but the underlying theory of data collection, organization and analysis is one of the most challenging topics for business students and practitioners. This user-friendly text and CD-ROM package will help you to develop strong skills in presenting and interpreting statistical information in a business or management environment. Based entirely on using Microsoft Excel rather than more complicated applications, it includes a clear guide to using Excel with the key functions employed in the book, a glossary of terms and

Statistical analysis of anomalous transport in resistive interchange turbulence

International Nuclear Information System (INIS)

Sugama, Hideo; Wakatani, Masahiro.

1992-01-01

A new anomalous transport model for resistive interchange turbulence is derived from statistical analysis applying two-scale direct-interaction approximation to resistive magnetohydrodynamic equations with a gravity term. Our model is similar to the K-ε model for eddy viscosity of turbulent shear flows in that anomalous transport coefficients are expressed in terms of by the turbulent kinetic energy K and its dissipation rate ε while K and ε are determined by transport equations. This anomalous transport model can describe some nonlocal effects such as those from boundary conditions which cannot be treated by conventional models based on the transport coefficients represented by locally determined plasma parameters. (author)

Development of computer-assisted instruction application for statistical data analysis android platform as learning resource

Science.gov (United States)

Hendikawati, P.; Arifudin, R.; Zahid, M. Z.

2018-03-01

This study aims to design an android Statistics Data Analysis application that can be accessed through mobile devices to making it easier for users to access. The Statistics Data Analysis application includes various topics of basic statistical along with a parametric statistics data analysis application. The output of this application system is parametric statistics data analysis that can be used for students, lecturers, and users who need the results of statistical calculations quickly and easily understood. Android application development is created using Java programming language. The server programming language uses PHP with the Code Igniter framework, and the database used MySQL. The system development methodology used is the Waterfall methodology with the stages of analysis, design, coding, testing, and implementation and system maintenance. This statistical data analysis application is expected to support statistical lecturing activities and make students easier to understand the statistical analysis of mobile devices.

Statistical analysis of metallicity in spiral galaxies

Energy Technology Data Exchange (ETDEWEB)

Galeotti, P [Consiglio Nazionale delle Ricerche, Turin (Italy). Lab. di Cosmo-Geofisica; Turin Univ. (Italy). Ist. di Fisica Generale)

1981-04-01

A principal component analysis of metallicity and other integral properties of 33 spiral galaxies is presented; the involved parameters are: morphological type, diameter, luminosity and metallicity. From the statistical analysis it is concluded that the sample has only two significant dimensions and additonal tests, involving different parameters, show similar results. Thus it seems that only type and luminosity are independent variables, being the other integral properties of spiral galaxies correlated with them.

Informing Evidence Based Decisions: Usage Statistics for Online Journal Databases

Directory of Open Access Journals (Sweden)

Alexei Botchkarev

2017-06-01

Full Text Available Abstract Objective – The primary objective was to examine online journal database usage statistics for a provincial ministry of health in the context of evidence based decision-making. In addition, the study highlights implementation of the Journal Access Centre (JAC that is housed and powered by the Ontario Ministry of Health and Long-Term Care (MOHLTC to inform health systems policy-making. Methods – This was a prospective case study using descriptive analysis of the JAC usage statistics of journal articles from January 2009 to September 2013. Results – JAC enables ministry employees to access approximately 12,000 journals with full-text articles. JAC usage statistics for the 2011-2012 calendar years demonstrate a steady level of activity in terms of searches, with monthly averages of 5,129. In 2009-2013, a total of 4,759 journal titles were accessed including 1,675 journals with full-text. Usage statistics demonstrate that the actual consumption was over 12,790 full-text downloaded articles or approximately 2,700 articles annually. Conclusion – JAC’s steady level of activities, revealed by the study, reflects continuous demand for JAC services and products. It testifies that access to online journal databases has become part of routine government knowledge management processes. MOHLTC’s broad area of responsibilities with dynamically changing priorities translates into the diverse information needs of its employees and a large set of required journals. Usage statistics indicate that MOHLTC information needs cannot be mapped to a reasonably compact set of “core” journals with a subsequent subscription to those.

Statistical flaws in design and analysis of fertility treatment studies on cryopreservation raise doubts on the conclusions

Science.gov (United States)

van Gelder, P.H.A.J.M.; Nijs, M.

2011-01-01

Decisions about pharmacotherapy are being taken by medical doctors and authorities based on comparative studies on the use of medications. In studies on fertility treatments in particular, the methodological quality is of utmost importance in the application of evidence-based medicine and systematic reviews. Nevertheless, flaws and omissions appear quite regularly in these types of studies. Current study aims to present an overview of some of the typical statistical flaws, illustrated by a number of example studies which have been published in peer reviewed journals. Based on an investigation of eleven studies at random selected on fertility treatments with cryopreservation, it appeared that the methodological quality of these studies often did not fulfil the required statistical criteria. The following statistical flaws were identified: flaws in study design, patient selection, and units of analysis or in the definition of the primary endpoints. Other errors could be found in p-value and power calculations or in critical p-value definitions. Proper interpretation of the results and/or use of these study results in a meta analysis should therefore be conducted with care. PMID:24753877

Statistical flaws in design and analysis of fertility treatment -studies on cryopreservation raise doubts on the conclusions.

Science.gov (United States)

van Gelder, P H A J M; Nijs, M

2011-01-01

Decisions about pharmacotherapy are being taken by medical doctors and authorities based on comparative studies on the use of medications. In studies on fertility treatments in particular, the methodological quality is of utmost -importance in the application of evidence-based medicine and systematic reviews. Nevertheless, flaws and omissions appear quite regularly in these types of studies. Current study aims to present an overview of some of the typical statistical flaws, illustrated by a number of example studies which have been published in peer reviewed journals. Based on an investigation of eleven studies at random selected on fertility treatments with cryopreservation, it appeared that the methodological quality of these studies often did not fulfil the -required statistical criteria. The following statistical flaws were identified: flaws in study design, patient selection, and units of analysis or in the definition of the primary endpoints. Other errors could be found in p-value and power calculations or in critical p-value definitions. Proper -interpretation of the results and/or use of these study results in a meta analysis should therefore be conducted with care.

The analysis of morphometric data on rocky mountain wolves and artic wolves using statistical method

Science.gov (United States)

Ammar Shafi, Muhammad; Saifullah Rusiman, Mohd; Hamzah, Nor Shamsidah Amir; Nor, Maria Elena; Ahmad, Noor’ani; Azia Hazida Mohamad Azmi, Nur; Latip, Muhammad Faez Ab; Hilmi Azman, Ahmad

2018-04-01

Morphometrics is a quantitative analysis depending on the shape and size of several specimens. Morphometric quantitative analyses are commonly used to analyse fossil record, shape and size of specimens and others. The aim of the study is to find the differences between rocky mountain wolves and arctic wolves based on gender. The sample utilised secondary data which included seven variables as independent variables and two dependent variables. Statistical modelling was used in the analysis such was the analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA). The results showed there exist differentiating results between arctic wolves and rocky mountain wolves based on independent factors and gender.

Statistical Analysis of Protein Ensembles

Science.gov (United States)

Máté, Gabriell; Heermann, Dieter

2014-04-01

As 3D protein-configuration data is piling up, there is an ever-increasing need for well-defined, mathematically rigorous analysis approaches, especially that the vast majority of the currently available methods rely heavily on heuristics. We propose an analysis framework which stems from topology, the field of mathematics which studies properties preserved under continuous deformations. First, we calculate a barcode representation of the molecules employing computational topology algorithms. Bars in this barcode represent different topological features. Molecules are compared through their barcodes by statistically determining the difference in the set of their topological features. As a proof-of-principle application, we analyze a dataset compiled of ensembles of different proteins, obtained from the Ensemble Protein Database. We demonstrate that our approach correctly detects the different protein groupings.

State analysis of BOP using statistical and heuristic methods

International Nuclear Information System (INIS)

Heo, Gyun Young; Chang, Soon Heung

2003-01-01

Under the deregulation environment, the performance enhancement of BOP in nuclear power plants is being highlighted. To analyze performance level of BOP, we use the performance test procedures provided from an authorized institution such as ASME. However, through plant investigation, it was proved that the requirements of the performance test procedures about the reliability and quantity of sensors was difficult to be satisfied. As a solution of this, state analysis method that are the expanded concept of signal validation, was proposed on the basis of the statistical and heuristic approaches. Authors recommended the statistical linear regression model by analyzing correlation among BOP parameters as a reference state analysis method. Its advantage is that its derivation is not heuristic, it is possible to calculate model uncertainty, and it is easy to apply to an actual plant. The error of the statistical linear regression model is below 3% under normal as well as abnormal system states. Additionally a neural network model was recommended since the statistical model is impossible to apply to the validation of all of the sensors and is sensitive to the outlier that is the signal located out of a statistical distribution. Because there are a lot of sensors need to be validated in BOP, wavelet analysis (WA) were applied as a pre-processor for the reduction of input dimension and for the enhancement of training accuracy. The outlier localization capability of WA enhanced the robustness of the neural network. The trained neural network restored the degraded signals to the values within ±3% of the true signals

Integrated Data Collection Analysis (IDCA) Program - Statistical Analysis of RDX Standard Data Sets

Energy Technology Data Exchange (ETDEWEB)

Sandstrom, Mary M. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Brown, Geoffrey W. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Preston, Daniel N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Pollard, Colin J. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Warner, Kirstin F. [Naval Surface Warfare Center (NSWC), Indian Head, MD (United States). Indian Head Division; Sorensen, Daniel N. [Naval Surface Warfare Center (NSWC), Indian Head, MD (United States). Indian Head Division; Remmers, Daniel L. [Naval Surface Warfare Center (NSWC), Indian Head, MD (United States). Indian Head Division; Phillips, Jason J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Shelley, Timothy J. [Air Force Research Lab. (AFRL), Tyndall AFB, FL (United States); Reyes, Jose A. [Applied Research Associates, Tyndall AFB, FL (United States); Hsu, Peter C. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Reynolds, John G. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2015-10-30

The Integrated Data Collection Analysis (IDCA) program is conducting a Proficiency Test for Small- Scale Safety and Thermal (SSST) testing of homemade explosives (HMEs). Described here are statistical analyses of the results for impact, friction, electrostatic discharge, and differential scanning calorimetry analysis of the RDX Type II Class 5 standard. The material was tested as a well-characterized standard several times during the proficiency study to assess differences among participants and the range of results that may arise for well-behaved explosive materials. The analyses show that there are detectable differences among the results from IDCA participants. While these differences are statistically significant, most of them can be disregarded for comparison purposes to assess potential variability when laboratories attempt to measure identical samples using methods assumed to be nominally the same. The results presented in this report include the average sensitivity results for the IDCA participants and the ranges of values obtained. The ranges represent variation about the mean values of the tests of between 26% and 42%. The magnitude of this variation is attributed to differences in operator, method, and environment as well as the use of different instruments that are also of varying age. The results appear to be a good representation of the broader safety testing community based on the range of methods, instruments, and environments included in the IDCA Proficiency Test.

Statistical analysis of RHIC beam position monitors performance

Science.gov (United States)

Calaga, R.; Tomás, R.

2004-04-01

A detailed statistical analysis of beam position monitors (BPM) performance at RHIC is a critical factor in improving regular operations and future runs. Robust identification of malfunctioning BPMs plays an important role in any orbit or turn-by-turn analysis. Singular value decomposition and Fourier transform methods, which have evolved as powerful numerical techniques in signal processing, will aid in such identification from BPM data. This is the first attempt at RHIC to use a large set of data to statistically enhance the capability of these two techniques and determine BPM performance. A comparison from run 2003 data shows striking agreement between the two methods and hence can be used to improve BPM functioning at RHIC and possibly other accelerators.

Statistical analysis of RHIC beam position monitors performance

Directory of Open Access Journals (Sweden)

R. Calaga

2004-04-01

Full Text Available A detailed statistical analysis of beam position monitors (BPM performance at RHIC is a critical factor in improving regular operations and future runs. Robust identification of malfunctioning BPMs plays an important role in any orbit or turn-by-turn analysis. Singular value decomposition and Fourier transform methods, which have evolved as powerful numerical techniques in signal processing, will aid in such identification from BPM data. This is the first attempt at RHIC to use a large set of data to statistically enhance the capability of these two techniques and determine BPM performance. A comparison from run 2003 data shows striking agreement between the two methods and hence can be used to improve BPM functioning at RHIC and possibly other accelerators.

On the structure of dynamic principal component analysis used in statistical process monitoring

DEFF Research Database (Denmark)

Vanhatalo, Erik; Kulahci, Murat; Bergquist, Bjarne

2017-01-01

When principal component analysis (PCA) is used for statistical process monitoring it relies on the assumption that data are time independent. However, industrial data will often exhibit serial correlation. Dynamic PCA (DPCA) has been suggested as a remedy for high-dimensional and time...... for determining the number of principal components to retain. The number of retained principal components is determined by visual inspection of the serial correlation in the squared prediction error statistic, Q (SPE), together with the cumulative explained variance of the model. The methods are illustrated using...... driven method to determine the maximum number of lags in DPCA with a foundation in multivariate time series analysis. The method is based on the behavior of the eigenvalues of the lagged autocorrelation and partial autocorrelation matrices. Given a specific lag structure we also propose a method...

Automated Analysis of 123I-beta-CIT SPECT Images with Statistical Probabilistic Anatomical Mapping

International Nuclear Information System (INIS)

Eo, Jae Seon; Lee, Hoyoung; Lee, Jae Sung; Kim, Yu Kyung; Jeon, Bumseok; Lee, Dong Soo

2014-01-01

Population-based statistical probabilistic anatomical maps have been used to generate probabilistic volumes of interest for analyzing perfusion and metabolic brain imaging. We investigated the feasibility of automated analysis for dopamine transporter images using this technique and evaluated striatal binding potentials in Parkinson's disease and Wilson's disease. We analyzed 2β-Carbomethoxy-3β-(4- 123 I-iodophenyl)tropane ( 123 I-beta-CIT) SPECT images acquired from 26 people with Parkinson's disease (M:F=11:15,mean age=49±12 years), 9 people with Wilson's disease (M: F=6:3, mean age=26±11 years) and 17 normal controls (M:F=5:12, mean age=39±16 years). A SPECT template was created using striatal statistical probabilistic map images. All images were spatially normalized onto the template, and probability-weighted regional counts in striatal structures were estimated. The binding potential was calculated using the ratio of specific and nonspecific binding activities at equilibrium. Voxel-based comparisons between groups were also performed using statistical parametric mapping. Qualitative assessment showed that spatial normalizations of the SPECT images were successful for all images. The striatal binding potentials of participants with Parkinson's disease and Wilson's disease were significantly lower than those of normal controls. Statistical parametric mapping analysis found statistically significant differences only in striatal regions in both disease groups compared to controls. We successfully evaluated the regional 123 I-beta-CIT distribution using the SPECT template and probabilistic map data automatically. This procedure allows an objective and quantitative comparison of the binding potential, which in this case showed a significantly decreased binding potential in the striata of patients with Parkinson's disease or Wilson's disease

Statistics Education Research in Malaysia and the Philippines: A Comparative Analysis

Science.gov (United States)

Reston, Enriqueta; Krishnan, Saras; Idris, Noraini

2014-01-01

This paper presents a comparative analysis of statistics education research in Malaysia and the Philippines by modes of dissemination, research areas, and trends. An electronic search for published research papers in the area of statistics education from 2000-2012 yielded 20 for Malaysia and 19 for the Philippines. Analysis of these papers showed…

Statistical analysis of next generation sequencing data

CERN Document Server

Nettleton, Dan

2014-01-01

Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized med...

Selected papers on analysis, probability, and statistics

CERN Document Server

Nomizu, Katsumi

1994-01-01

This book presents papers that originally appeared in the Japanese journal Sugaku. The papers fall into the general area of mathematical analysis as it pertains to probability and statistics, dynamical systems, differential equations and analytic function theory. Among the topics discussed are: stochastic differential equations, spectra of the Laplacian and Schrödinger operators, nonlinear partial differential equations which generate dissipative dynamical systems, fractal analysis on self-similar sets and the global structure of analytic functions.

A statistically self-consistent type Ia supernova data analysis

International Nuclear Information System (INIS)

Lago, B.L.; Calvao, M.O.; Joras, S.E.; Reis, R.R.R.; Waga, I.; Giostri, R.

2011-01-01

Full text: The type Ia supernovae are one of the main cosmological probes nowadays and are used as standardized candles in distance measurements. The standardization processes, among which SALT2 and MLCS2k2 are the most used ones, are based on empirical relations and leave room for a residual dispersion in the light curves of the supernovae. This dispersion is introduced in the chi squared used to fit the parameters of the model in the expression for the variance of the data, as an attempt to quantify our ignorance in modeling the supernovae properly. The procedure used to assign a value to this dispersion is statistically inconsistent and excludes the possibility of comparing different cosmological models. In addition, the SALT2 light curve fitter introduces parameters on the model for the variance that are also used in the model for the data. In the chi squared statistics context the minimization of such a quantity yields, in the best case scenario, a bias. An iterative method has been developed in order to perform the minimization of this chi squared but it is not well grounded, although it is used by several groups. We propose an analysis of the type Ia supernovae data that is based on the likelihood itself and makes it possible to address both inconsistencies mentioned above in a straightforward way. (author)

Statistical Analysis of Data with Non-Detectable Values

Energy Technology Data Exchange (ETDEWEB)

Frome, E.L.

2004-08-26

Environmental exposure measurements are, in general, positive and may be subject to left censoring, i.e. the measured value is less than a ''limit of detection''. In occupational monitoring, strategies for assessing workplace exposures typically focus on the mean exposure level or the probability that any measurement exceeds a limit. A basic problem of interest in environmental risk assessment is to determine if the mean concentration of an analyte is less than a prescribed action level. Parametric methods, used to determine acceptable levels of exposure, are often based on a two parameter lognormal distribution. The mean exposure level and/or an upper percentile (e.g. the 95th percentile) are used to characterize exposure levels, and upper confidence limits are needed to describe the uncertainty in these estimates. In certain situations it is of interest to estimate the probability of observing a future (or ''missed'') value of a lognormal variable. Statistical methods for random samples (without non-detects) from the lognormal distribution are well known for each of these situations. In this report, methods for estimating these quantities based on the maximum likelihood method for randomly left censored lognormal data are described and graphical methods are used to evaluate the lognormal assumption. If the lognormal model is in doubt and an alternative distribution for the exposure profile of a similar exposure group is not available, then nonparametric methods for left censored data are used. The mean exposure level, along with the upper confidence limit, is obtained using the product limit estimate, and the upper confidence limit on the 95th percentile (i.e. the upper tolerance limit) is obtained using a nonparametric approach. All of these methods are well known but computational complexity has limited their use in routine data analysis with left censored data. The recent development of the R environment for statistical

Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model

Directory of Open Access Journals (Sweden)

Zhai Chengxiang

2010-05-01

Full Text Available Abstract Background Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns. The interpretation of these gene lists is generally achieved by extracting concepts overrepresented in the gene lists. This analysis often depends on manual annotation of genes based on controlled vocabularies, in particular, Gene Ontology (GO. However, the annotation of genes is a labor-intensive process; and the vocabularies are generally incomplete, leaving some important biological domains inadequately covered. Results We propose a statistical method that uses the primary literature, i.e. free-text, as the source to perform overrepresentation analysis. The method is based on a statistical framework of mixture model and addresses the methodological flaws in several existing programs. We implemented this method within a literature mining system, BeeSpace, taking advantage of its analysis environment and added features that facilitate the interactive analysis of gene sets. Through experimentation with several datasets, we showed that our program can effectively summarize the important conceptual themes of large gene sets, even when traditional GO-based analysis does not yield informative results. Conclusions We conclude that the current work will provide biologists with a tool that effectively complements the existing ones for overrepresentation analysis from genomic experiments. Our program, Genelist Analyzer, is freely available at: http://workerbee.igb.uiuc.edu:8080/BeeSpace/Search.jsp

Bringing statistics up to speed with data in analysis of lymphocyte motility.

Science.gov (United States)

Letendre, Kenneth; Donnadieu, Emmanuel; Moses, Melanie E; Cannon, Judy L

2015-01-01

Two-photon (2P) microscopy provides immunologists with 3D video of the movement of lymphocytes in vivo. Motility parameters extracted from these videos allow detailed analysis of lymphocyte motility in lymph nodes and peripheral tissues. However, standard parametric statistical analyses such as the Student's t-test are often used incorrectly, and fail to take into account confounds introduced by the experimental methods, potentially leading to erroneous conclusions about T cell motility. Here, we compare the motility of WT T cell versus PKCθ-/-, CARMA1-/-, CCR7-/-, and PTX-treated T cells. We show that the fluorescent dyes used to label T cells have significant effects on T cell motility, and we demonstrate the use of factorial ANOVA as a statistical tool that can control for these effects. In addition, researchers often choose between the use of "cell-based" parameters by averaging multiple steps of a single cell over time (e.g. cell mean speed), or "step-based" parameters, in which all steps of a cell population (e.g. instantaneous speed) are grouped without regard for the cell track. Using mixed model ANOVA, we show that we can maintain cell-based analyses without losing the statistical power of step-based data. We find that as we use additional levels of statistical control, we can more accurately estimate the speed of T cells as they move in lymph nodes as well as measure the impact of individual signaling molecules on T cell motility. As there is increasing interest in using computational modeling to understand T cell behavior in in vivo, these quantitative measures not only give us a better determination of actual T cell movement, they may prove crucial for models to generate accurate predictions about T cell behavior.

The system for statistical analysis of logistic information

Directory of Open Access Journals (Sweden)

Khayrullin Rustam Zinnatullovich

2015-05-01

Full Text Available The current problem for managers in logistic and trading companies is the task of improving the operational business performance and developing the logistics support of sales. The development of logistics sales supposes development and implementation of a set of works for the development of the existing warehouse facilities, including both a detailed description of the work performed, and the timing of their implementation. Logistics engineering of warehouse complex includes such tasks as: determining the number and the types of technological zones, calculation of the required number of loading-unloading places, development of storage structures, development and pre-sales preparation zones, development of specifications of storage types, selection of loading-unloading equipment, detailed planning of warehouse logistics system, creation of architectural-planning decisions, selection of information-processing equipment, etc. The currently used ERP and WMS systems did not allow us to solve the full list of logistics engineering problems. In this regard, the development of specialized software products, taking into account the specifics of warehouse logistics, and subsequent integration of these software with ERP and WMS systems seems to be a current task. In this paper we suggest a system of statistical analysis of logistics information, designed to meet the challenges of logistics engineering and planning. The system is based on the methods of statistical data processing.The proposed specialized software is designed to improve the efficiency of the operating business and the development of logistics support of sales. The system is based on the methods of statistical data processing, the methods of assessment and prediction of logistics performance, the methods for the determination and calculation of the data required for registration, storage and processing of metal products, as well as the methods for planning the reconstruction and development

Statistical Analysis of the Polarimetric Cloud Analysis and Seeding Test (POLCAST) Field Projects

Science.gov (United States)

Ekness, Jamie Lynn

The North Dakota farming industry brings in more than $4.1 billion annually in cash receipts. Unfortunately, agriculture sales vary significantly from year to year, which is due in large part to weather events such as hail storms and droughts. One method to mitigate drought is to use hygroscopic seeding to increase the precipitation efficiency of clouds. The North Dakota Atmospheric Research Board (NDARB) sponsored the Polarimetric Cloud Analysis and Seeding Test (POLCAST) research project to determine the effectiveness of hygroscopic seeding in North Dakota. The POLCAST field projects obtained airborne and radar observations, while conducting randomized cloud seeding. The Thunderstorm Identification Tracking and Nowcasting (TITAN) program is used to analyze radar data (33 usable cases) in determining differences in the duration of the storm, rain rate and total rain amount between seeded and non-seeded clouds. The single ratio of seeded to non-seeded cases is 1.56 (0.28 mm/0.18 mm) or 56% increase for the average hourly rainfall during the first 60 minutes after target selection. A seeding effect is indicated with the lifetime of the storms increasing by 41 % between seeded and non-seeded clouds for the first 60 minutes past seeding decision. A double ratio statistic, a comparison of radar derived rain amount of the last 40 minutes of a case (seed/non-seed), compared to the first 20 minutes (seed/non-seed), is used to account for the natural variability of the cloud system and gives a double ratio of 1.85. The Mann-Whitney test on the double ratio of seeded to non-seeded cases (33 cases) gives a significance (p-value) of 0.063. Bootstrapping analysis of the POLCAST set indicates that 50 cases would provide statistically significant results based on the Mann-Whitney test of the double ratio. All the statistical analysis conducted on the POLCAST data set show that hygroscopic seeding in North Dakota does increase precipitation. While an additional POLCAST field

Comparative analysis of positive and negative attitudes toward statistics

Science.gov (United States)

Ghulami, Hassan Rahnaward; Ab Hamid, Mohd Rashid; Zakaria, Roslinazairimah

2015-02-01

Many statistics lecturers and statistics education researchers are interested to know the perception of their students' attitudes toward statistics during the statistics course. In statistics course, positive attitude toward statistics is a vital because it will be encourage students to get interested in the statistics course and in order to master the core content of the subject matters under study. Although, students who have negative attitudes toward statistics they will feel depressed especially in the given group assignment, at risk for failure, are often highly emotional, and could not move forward. Therefore, this study investigates the students' attitude towards learning statistics. Six latent constructs have been the measurement of students' attitudes toward learning statistic such as affect, cognitive competence, value, difficulty, interest, and effort. The questionnaire was adopted and adapted from the reliable and validate instrument of Survey of Attitudes towards Statistics (SATS). This study is conducted among engineering undergraduate engineering students in the university Malaysia Pahang (UMP). The respondents consist of students who were taking the applied statistics course from different faculties. From the analysis, it is found that the questionnaire is acceptable and the relationships among the constructs has been proposed and investigated. In this case, students show full effort to master the statistics course, feel statistics course enjoyable, have confidence that they have intellectual capacity, and they have more positive attitudes then negative attitudes towards statistics learning. In conclusion in terms of affect, cognitive competence, value, interest and effort construct the positive attitude towards statistics was mostly exhibited. While negative attitudes mostly exhibited by difficulty construct.

Nonparametric and group-based person-fit statistics : a validity study and an empirical example

NARCIS (Netherlands)

Meijer, R.R.

1994-01-01

In person-fit analysis, the object is to investigate whether an item score pattern is improbable given the item score patterns of the other persons in the group or given what is expected on the basis of a test model. In this study, several existing group-based statistics to detect such improbable

mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry.

Science.gov (United States)

Teo, Guoshou; Kim, Sinae; Tsou, Chih-Chiang; Collins, Ben; Gingras, Anne-Claude; Nesvizhskii, Alexey I; Choi, Hyungwon

2015-11-03

Data independent acquisition (DIA) mass spectrometry is an emerging technique that offers more complete detection and quantification of peptides and proteins across multiple samples. DIA allows fragment-level quantification, which can be considered as repeated measurements of the abundance of the corresponding peptides and proteins in the downstream statistical analysis. However, few statistical approaches are available for aggregating these complex fragment-level data into peptide- or protein-level statistical summaries. In this work, we describe a software package, mapDIA, for statistical analysis of differential protein expression using DIA fragment-level intensities. The workflow consists of three major steps: intensity normalization, peptide/fragment selection, and statistical analysis. First, mapDIA offers normalization of fragment-level intensities by total intensity sums as well as a novel alternative normalization by local intensity sums in retention time space. Second, mapDIA removes outlier observations and selects peptides/fragments that preserve the major quantitative patterns across all samples for each protein. Last, using the selected fragments and peptides, mapDIA performs model-based statistical significance analysis of protein-level differential expression between specified groups of samples. Using a comprehensive set of simulation datasets, we show that mapDIA detects differentially expressed proteins with accurate control of the false discovery rates. We also describe the analysis procedure in detail using two recently published DIA datasets generated for 14-3-3β dynamic interaction network and prostate cancer glycoproteome. The software was written in C++ language and the source code is available for free through SourceForge website http://sourceforge.net/projects/mapdia/.This article is part of a Special Issue entitled: Computational Proteomics. Copyright © 2015 Elsevier B.V. All rights reserved.

Gauging Skills of Hospital Security Personnel: a Statistically-driven, Questionnaire-based Approach.

Science.gov (United States)

Rinkoo, Arvind Vashishta; Mishra, Shubhra; Rahesuddin; Nabi, Tauqeer; Chandra, Vidha; Chandra, Hem

2013-01-01

This study aims to gauge the technical and soft skills of the hospital security personnel so as to enable prioritization of their training needs. A cross sectional questionnaire based study was conducted in December 2011. Two separate predesigned and pretested questionnaires were used for gauging soft skills and technical skills of the security personnel. Extensive statistical analysis, including Multivariate Analysis (Pillai-Bartlett trace along with Multi-factorial ANOVA) and Post-hoc Tests (Bonferroni Test) was applied. The 143 participants performed better on the soft skills front with an average score of 6.43 and standard deviation of 1.40. The average technical skills score was 5.09 with a standard deviation of 1.44. The study avowed a need for formal hands on training with greater emphasis on technical skills. Multivariate analysis of the available data further helped in identifying 20 security personnel who should be prioritized for soft skills training and a group of 36 security personnel who should receive maximum attention during technical skills training. This statistically driven approach can be used as a prototype by healthcare delivery institutions worldwide, after situation specific customizations, to identify the training needs of any category of healthcare staff.

Voxel based statistical analysis method for microPET studies to assess the cerebral glucose metabolism in cat deafness model: comparison to ROI based method

International Nuclear Information System (INIS)

Kim, Jin Su; Lee, Jae Sung; Park, Min Hyun; Lee, Jong Jin; Kang, Hye Jin; Lee, Hyo Jeong; Oh, Seung Ha; Kim, Chong Sun; Jung, June Key; Lee, Myung Chul; Lee, Dong Soo; Lim, Sang Moo

2005-01-01

Imaging research on the brain of sensory-deprived cats using small animal PET scanner has gained interest since the abundant information about the sensory system of ths animal is available and close examination of the brain is possible due to larger size of its brain than mouse or rat. In this study, we have established the procedures for 3D voxel-based statistical analysis (SPM) of FDG PET image of cat brain, and confirmed using ROI based-method. FDG PET scans of 4 normal and 4 deaf cats were acquired for 30 minutes using microPET R4 scanner. Only the brain cortices were extracted using a masking and threshold method to facilitate spatial normalization. After spatial normalization and smoothing, 3D voxel-wise and ROI based t-test were performed to identify the regions with significant different FDG uptake between the normal and deaf cats. In ROI analysis, 26 ROIs were drawn on both hemispheres, and regional mean pixel value in each ROI was normalized to the global mean of the brain. Cat brains were spatially normalized well onto the target brain due to the removal of background activity. When cerebral glucose metabolism of deaf cats were compared to the normal controls after removing the effects of the global count, the glucose metabolism in the auditory cortex, head of caudate nucleus, and thalamus in both hemispheres of the deaf cats was significantly lower than that of the controls (P<0.01). No area showed a significantly increased metabolism in the deaf cats even in higher significance level (P<0.05). ROI analysis also showed significant reduction of glucose metabolism in the same region. This study established and confirmed a method for voxel-based analysis of animal PET data of cat brain, which showed high localization accuracy and specificity and was useful for examining the cerebral glucose metabolism in a cat cortical deafness model

Voxel based statistical analysis method for microPET studies to assess the cerebral glucose metabolism in cat deafness model: comparison to ROI based method

Energy Technology Data Exchange (ETDEWEB)

Kim, Jin Su; Lee, Jae Sung; Park, Min Hyun; Lee, Jong Jin; Kang, Hye Jin; Lee, Hyo Jeong; Oh, Seung Ha; Kim, Chong Sun; Jung, June Key; Lee, Myung Chul; Lee, Dong Soo [Seoul National University College of Medicine, Seoul (Korea, Republic of); Lim, Sang Moo [KIRAMS, Seoul (Korea, Republic of)

2005-07-01

Imaging research on the brain of sensory-deprived cats using small animal PET scanner has gained interest since the abundant information about the sensory system of ths animal is available and close examination of the brain is possible due to larger size of its brain than mouse or rat. In this study, we have established the procedures for 3D voxel-based statistical analysis (SPM) of FDG PET image of cat brain, and confirmed using ROI based-method. FDG PET scans of 4 normal and 4 deaf cats were acquired for 30 minutes using microPET R4 scanner. Only the brain cortices were extracted using a masking and threshold method to facilitate spatial normalization. After spatial normalization and smoothing, 3D voxel-wise and ROI based t-test were performed to identify the regions with significant different FDG uptake between the normal and deaf cats. In ROI analysis, 26 ROIs were drawn on both hemispheres, and regional mean pixel value in each ROI was normalized to the global mean of the brain. Cat brains were spatially normalized well onto the target brain due to the removal of background activity. When cerebral glucose metabolism of deaf cats were compared to the normal controls after removing the effects of the global count, the glucose metabolism in the auditory cortex, head of caudate nucleus, and thalamus in both hemispheres of the deaf cats was significantly lower than that of the controls (P<0.01). No area showed a significantly increased metabolism in the deaf cats even in higher significance level (P<0.05). ROI analysis also showed significant reduction of glucose metabolism in the same region. This study established and confirmed a method for voxel-based analysis of animal PET data of cat brain, which showed high localization accuracy and specificity and was useful for examining the cerebral glucose metabolism in a cat cortical deafness model.

Vapor Pressure Data Analysis and Statistics

Science.gov (United States)

2016-12-01

near 8, 2000, and 200, respectively. The A (or a) value is directly related to vapor pressure and will be greater for high vapor pressure materials...1, (10) where n is the number of data points, Yi is the natural logarithm of the i th experimental vapor pressure value, and Xi is the...VAPOR PRESSURE DATA ANALYSIS AND STATISTICS ECBC-TR-1422 Ann Brozena RESEARCH AND TECHNOLOGY DIRECTORATE

Applications of modern statistical methods to analysis of data in physical science

Science.gov (United States)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance

Statistical analysis of planktic foraminifera of the surface Continental ...

African Journals Online (AJOL)

Planktic foraminiferal assemblage recorded from selected samples obtained from shallow continental shelf sediments off southwestern Nigeria were subjected to statistical analysis. The Principal Component Analysis (PCA) was used to determine variants of planktic parameters. Values obtained for these parameters were ...

Detecting fire in video stream using statistical analysis

Directory of Open Access Journals (Sweden)

Koplík Karel

2017-01-01

Full Text Available The real time fire detection in video stream is one of the most interesting problems in computer vision. In fact, in most cases it would be nice to have fire detection algorithm implemented in usual industrial cameras and/or to have possibility to replace standard industrial cameras with one implementing the fire detection algorithm. In this paper, we present new algorithm for detecting fire in video. The algorithm is based on tracking suspicious regions in time with statistical analysis of their trajectory. False alarms are minimized by combining multiple detection criteria: pixel brightness, trajectories of suspicious regions for evaluating characteristic fire flickering and persistence of alarm state in sequence of frames. The resulting implementation is fast and therefore can run on wide range of affordable hardware.

Imaging mass spectrometry statistical analysis.

Science.gov (United States)

Jones, Emrys A; Deininger, Sören-Oliver; Hogendoorn, Pancras C W; Deelder, André M; McDonnell, Liam A

2012-08-30

Imaging mass spectrometry is increasingly used to identify new candidate biomarkers. This clinical application of imaging mass spectrometry is highly multidisciplinary: expertise in mass spectrometry is necessary to acquire high quality data, histology is required to accurately label the origin of each pixel's mass spectrum, disease biology is necessary to understand the potential meaning of the imaging mass spectrometry results, and statistics to assess the confidence of any findings. Imaging mass spectrometry data analysis is further complicated because of the unique nature of the data (within the mass spectrometry field); several of the assumptions implicit in the analysis of LC-MS/profiling datasets are not applicable to imaging. The very large size of imaging datasets and the reporting of many data analysis routines, combined with inadequate training and accessible reviews, have exacerbated this problem. In this paper we provide an accessible review of the nature of imaging data and the different strategies by which the data may be analyzed. Particular attention is paid to the assumptions of the data analysis routines to ensure that the reader is apprised of their correct usage in imaging mass spectrometry research. Copyright © 2012 Elsevier B.V. All rights reserved.

Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory

Science.gov (United States)

Ren, W. X.; Lin, Y. Q.; Fang, S. E.

2011-11-01

One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.

Criminal victimization in Ukraine: analysis of statistical data

Directory of Open Access Journals (Sweden)

Serhiy Nezhurbida

2007-12-01

Full Text Available The article is based on the analysis of statistical data provided by law-enforcement, judicial and other bodies of Ukraine. The given analysis allows us to give an accurate quantity of a current status of crime victimization in Ukraine, to characterize its basic features (level, rate, structure, dynamics, and etc.. L’article se concentre sur l’analyse des données statystiques fournies par les institutions de contrôle sociale (forces de police et magistrature et par d’autres organes institutionnels ukrainiens. Les analyses effectuées attirent l'attention sur la situation actuelle des victimes du crime en Ukraine et aident à délinéer leur principales caractéristiques (niveau, taux, structure, dynamiques, etc.L’articolo si basa sull’analisi dei dati statistici forniti dalle agenzie del controllo sociale (forze dell'ordine e magistratura e da altri organi istituzionali ucraini. Le analisi effettuate forniscono molte informazioni sulla situazione attuale delle vittime del crimine in Ucraina e aiutano a delinearne le caratteristiche principali (livello, tasso, struttura, dinamiche, ecc..

Data Analysis & Statistical Methods for Command File Errors

Science.gov (United States)

Meshkat, Leila; Waggoner, Bruce; Bryant, Larry

2014-01-01

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.

Western classical music development: a statistical analysis of composers similarity, differentiation and evolution.

Science.gov (United States)

Georges, Patrick

2017-01-01

This paper proposes a statistical analysis that captures similarities and differences between classical music composers with the eventual aim to understand why particular composers 'sound' different even if their 'lineages' (influences network) are similar or why they 'sound' alike if their 'lineages' are different. In order to do this we use statistical methods and measures of association or similarity (based on presence/absence of traits such as specific 'ecological' characteristics and personal musical influences) that have been developed in biosystematics, scientometrics, and bibliographic coupling. This paper also represents a first step towards a more ambitious goal of developing an evolutionary model of Western classical music.

Applied Behavior Analysis and Statistical Process Control?

Science.gov (United States)

Hopkins, B. L.

1995-01-01

Incorporating statistical process control (SPC) methods into applied behavior analysis is discussed. It is claimed that SPC methods would likely reduce applied behavior analysts' intimate contacts with problems and would likely yield poor treatment and research decisions. Cases and data presented by Pfadt and Wheeler (1995) are cited as examples.…

A framework for the economic analysis of data collection methods for vital statistics.

Science.gov (United States)

Jimenez-Soto, Eliana; Hodge, Andrew; Nguyen, Kim-Huong; Dettrick, Zoe; Lopez, Alan D

2014-01-01

Over recent years there has been a strong movement towards the improvement of vital statistics and other types of health data that inform evidence-based policies. Collecting such data is not cost free. To date there is no systematic framework to guide investment decisions on methods of data collection for vital statistics or health information in general. We developed a framework to systematically assess the comparative costs and outcomes/benefits of the various data methods for collecting vital statistics. The proposed framework is four-pronged and utilises two major economic approaches to systematically assess the available data collection methods: cost-effectiveness analysis and efficiency analysis. We built a stylised example of a hypothetical low-income country to perform a simulation exercise in order to illustrate an application of the framework. Using simulated data, the results from the stylised example show that the rankings of the data collection methods are not affected by the use of either cost-effectiveness or efficiency analysis. However, the rankings are affected by how quantities are measured. There have been several calls for global improvements in collecting useable data, including vital statistics, from health information systems to inform public health policies. Ours is the first study that proposes a systematic framework to assist countries undertake an economic evaluation of DCMs. Despite numerous challenges, we demonstrate that a systematic assessment of outputs and costs of DCMs is not only necessary, but also feasible. The proposed framework is general enough to be easily extended to other areas of health information.

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

Science.gov (United States)

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

2016-01-13

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

Experimental design techniques in statistical practice a practical software-based approach

CERN Document Server

Gardiner, W P

1998-01-01

Provides an introduction to the diverse subject area of experimental design, with many practical and applicable exercises to help the reader understand, present and analyse the data. The pragmatic approach offers technical training for use of designs and teaches statistical and non-statistical skills in design and analysis of project studies throughout science and industry. Provides an introduction to the diverse subject area of experimental design and includes practical and applicable exercises to help understand, present and analyse the data Offers technical training for use of designs and teaches statistical and non-statistical skills in design and analysis of project studies throughout science and industry Discusses one-factor designs and blocking designs, factorial experimental designs, Taguchi methods and response surface methods, among other topics.

Statistical methods in epidemiology. VII. An overview of the chi2 test for 2 x 2 contingency table analysis.

Science.gov (United States)

Rigby, A S

2001-11-10

The odds ratio is an appropriate method of analysis for data in 2 x 2 contingency tables. However, other methods of analysis exist. One such method is based on the chi2 test of goodness-of-fit. Key players in the development of statistical theory include Pearson, Fisher and Yates. Data are presented in the form of 2 x 2 contingency tables and a method of analysis based on the chi2 test is introduced. There are many variations of the basic test statistic, one of which is the chi2 test with Yates' continuity correction. The usefulness (or not) of Yates' continuity correction is discussed. Problems of interpretation when the method is applied to k x m tables are highlighted. Some properties of the chi2 the test are illustrated by taking examples from the author's teaching experiences. Journal editors should be encouraged to give both observed and expected cell frequencies so that better information comes out of the chi2 test statistic.

Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale

Science.gov (United States)

Oliveira, Sérgio C.; Zêzere, José L.; Lajas, Sara; Melo, Raquel

2017-07-01

Approaches used to assess shallow slide susceptibility at the basin scale are conceptually different depending on the use of statistical or physically based methods. The former are based on the assumption that the same causes are more likely to produce the same effects, whereas the latter are based on the comparison between forces which tend to promote movement along the slope and the counteracting forces that are resistant to motion. Within this general framework, this work tests two hypotheses: (i) although conceptually and methodologically distinct, the statistical and deterministic methods generate similar shallow slide susceptibility results regarding the model's predictive capacity and spatial agreement; and (ii) the combination of shallow slide susceptibility maps obtained with statistical and physically based methods, for the same study area, generate a more reliable susceptibility model for shallow slide occurrence. These hypotheses were tested at a small test site (13.9 km2) located north of Lisbon (Portugal), using a statistical method (the information value method, IV) and a physically based method (the infinite slope method, IS). The landslide susceptibility maps produced with the statistical and deterministic methods were combined into a new landslide susceptibility map. The latter was based on a set of integration rules defined by the cross tabulation of the susceptibility classes of both maps and analysis of the corresponding contingency tables. The results demonstrate a higher predictive capacity of the new shallow slide susceptibility map, which combines the independent results obtained with statistical and physically based models. Moreover, the combination of the two models allowed the identification of areas where the results of the information value and the infinite slope methods are contradictory. Thus, these areas were classified as uncertain and deserve additional investigation at a more detailed scale.

Statistical analysis of JET disruptions

International Nuclear Information System (INIS)

Tanga, A.; Johnson, M.F.

1991-07-01

In the operation of JET and of any tokamak many discharges are terminated by a major disruption. The disruptive termination of a discharge is usually an unwanted event which may cause damage to the structure of the vessel. In a reactor disruptions are potentially a very serious problem, hence the importance of studying them and devising methods to avoid disruptions. Statistical information has been collected about the disruptions which have occurred at JET over a long span of operations. The analysis is focused on the operational aspects of the disruptions rather than on the underlining physics. (Author)

Statistical method application to knowledge base building for reactor accident diagnostic system

International Nuclear Information System (INIS)

Yoshida, Kazuo; Yokobayashi, Masao; Matsumoto, Kiyoshi; Kohsaka, Atsuo

1989-01-01

In the development of a knowledge based expert system, one of key issues is how to build the knowledge base (KB) in an efficient way with keeping the objectivity of KB. In order to solve this issue, an approach has been proposed to build a prototype KB systematically by a statistical method, factor analysis. For the verification of this approach, factor analysis was applied to build a prototype KB for the JAERI expert system DISKET. To this end, alarm and process information was generated by a PWR simulator and the factor analysis was applied to this information to define taxonomy of accident hypotheses and to extract rules for each hypothesis. The prototype KB thus built was tested through inferring against several types of transients including double-failures. In each diagnosis, the transient type was well identified. Furthermore, newly introduced standards for rule extraction showed good effects on the enhancement of the performance of prototype KB. (author)

Simulation Experiments in Practice : Statistical Design and Regression Analysis

NARCIS (Netherlands)

Kleijnen, J.P.C.

2007-01-01

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. Statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic

Statistical analysis of the Ft. Calhoun reactor coolant pump system

International Nuclear Information System (INIS)

Patel, Bimal; Heising, C.D.

1997-01-01

In engineering science, statistical quality control techniques have traditionally been applied to control manufacturing processes. An application to commercial nuclear power plant maintenance and control is presented that can greatly improve plant safety. As a demonstration of such an approach, a specific system is analyzed: the reactor coolant pumps (RCPs) of the Ft. Calhoun nuclear power plant. This research uses capability analysis, Shewhart X-bar, R charts, canonical correlation methods, and design of experiments to analyze the process for the state of statistical control. The results obtained show that six out of ten parameters are under control specification limits and four parameters are not in the state of statistical control. The analysis shows that statistical process control methods can be applied as an early warning system capable of identifying significant equipment problems well in advance of traditional control room alarm indicators. Such a system would provide operators with ample time to respond to possible emergency situations and thus improve plant safety and reliability. (Author)

Recognizing stationary and locomotion activities using combinational of spectral analysis with statistical descriptors features

Science.gov (United States)

Zainudin, M. N. Shah; Sulaiman, Md Nasir; Mustapha, Norwati; Perumal, Thinagaran

2017-10-01

Prior knowledge in pervasive computing recently garnered a lot of attention due to its high demand in various application domains. Human activity recognition (HAR) considered as the applications that are widely explored by the expertise that provides valuable information to the human. Accelerometer sensor-based approach is utilized as devices to undergo the research in HAR since their small in size and this sensor already build-in in the various type of smartphones. However, the existence of high inter-class similarities among the class tends to degrade the recognition performance. Hence, this work presents the method for activity recognition using our proposed features from combinational of spectral analysis with statistical descriptors that able to tackle the issue of differentiating stationary and locomotion activities. The noise signal is filtered using Fourier Transform before it will be extracted using two different groups of features, spectral frequency analysis, and statistical descriptors. Extracted signal later will be classified using random forest ensemble classifier models. The recognition results show the good accuracy performance for stationary and locomotion activities based on USC HAD datasets.

Research and Development of Statistical Analysis Software System of Maize Seedling Experiment

OpenAIRE

Hui Cao

2014-01-01

In this study, software engineer measures were used to develop a set of software system for maize seedling experiments statistics and analysis works. During development works, B/S structure software design method was used and a set of statistics indicators for maize seedling evaluation were established. The experiments results indicated that this set of software system could finish quality statistics and analysis for maize seedling very well. The development of this software system explored a...

Statistical-techniques-based computer-aided diagnosis (CAD) using texture feature analysis: application in computed tomography (CT) imaging to fatty liver disease

Science.gov (United States)

Chung, Woon-Kwan; Park, Hyong-Hu; Im, In-Chul; Lee, Jae-Seung; Goo, Eun-Hoe; Dong, Kyung-Rae

2012-09-01

This paper proposes a computer-aided diagnosis (CAD) system based on texture feature analysis and statistical wavelet transformation technology to diagnose fatty liver disease with computed tomography (CT) imaging. In the target image, a wavelet transformation was performed for each lesion area to set the region of analysis (ROA, window size: 50 × 50 pixels) and define the texture feature of a pixel. Based on the extracted texture feature values, six parameters (average gray level, average contrast, relative smoothness, skewness, uniformity, and entropy) were determined to calculate the recognition rate for a fatty liver. In addition, a multivariate analysis of the variance (MANOVA) method was used to perform a discriminant analysis to verify the significance of the extracted texture feature values and the recognition rate for a fatty liver. According to the results, each texture feature value was significant for a comparison of the recognition rate for a fatty liver ( p fatty liver had the same scale as that for the F-value, showing 100% (average gray level) at the maximum and 80% (average contrast) at the minimum. Therefore, the recognition rate is believed to be a useful clinical value for the automatic detection and computer-aided diagnosis (CAD) using the texture feature value. Nevertheless, further study on various diseases and singular diseases will be needed in the future.

Statistical inference based on latent ability estimates

NARCIS (Netherlands)

Hoijtink, H.J.A.; Boomsma, A.

The quality of approximations to first and second order moments (e.g., statistics like means, variances, regression coefficients) based on latent ability estimates is being discussed. The ability estimates are obtained using either the Rasch, oi the two-parameter logistic model. Straightforward use

Assessment of Problem-Based Learning in the Undergraduate Statistics Course

Science.gov (United States)

Karpiak, Christie P.

2011-01-01

Undergraduate psychology majors (N = 51) at a mid-sized private university took a statistics examination on the first day of the research methods course, a course for which a grade of "C" or higher in statistics is a prerequisite. Students who had taken a problem-based learning (PBL) section of the statistics course (n = 15) were compared to those…

Statistical lamb wave localization based on extreme value theory

Science.gov (United States)

Harley, Joel B.

2018-04-01

Guided wave localization methods based on delay-and-sum imaging, matched field processing, and other techniques have been designed and researched to create images that locate and describe structural damage. The maximum value of these images typically represent an estimated damage location. Yet, it is often unclear if this maximum value, or any other value in the image, is a statistically significant indicator of damage. Furthermore, there are currently few, if any, approaches to assess the statistical significance of guided wave localization images. As a result, we present statistical delay-and-sum and statistical matched field processing localization methods to create statistically significant images of damage. Our framework uses constant rate of false alarm statistics and extreme value theory to detect damage with little prior information. We demonstrate our methods with in situ guided wave data from an aluminum plate to detect two 0.75 cm diameter holes. Our results show an expected improvement in statistical significance as the number of sensors increase. With seventeen sensors, both methods successfully detect damage with statistical significance.

StOCNET : Software for the statistical analysis of social networks

NARCIS (Netherlands)

Huisman, M.; van Duijn, M.A.J.

2003-01-01

StOCNET3 is an open software system in a Windows environment for the advanced statistical analysis of social networks. It provides a platform to make a number of recently developed and therefore not (yet) standard statistical methods available to a wider audience. A flexible user interface utilizing

AutoBayes: A System for Generating Data Analysis Programs from Statistical Models

OpenAIRE

Fischer, Bernd; Schumann, Johann

2003-01-01

Data analysis is an important scientific task which is required whenever information needs to be extracted from raw data. Statistical approaches to data analysis, which use methods from probability theory and numerical analysis, are well-founded but dificult to implement: the development of a statistical data analysis program for any given application is time-consuming and requires substantial knowledge and experience in several areas. In this paper, we describe AutoBayes, a program synthesis...

The International Coal Statistics Data Base program maintenance guide

International Nuclear Information System (INIS)

1991-06-01

The International Coal Statistics Data Base (ICSD) is a microcomputer-based system which contains information related to international coal trade. This includes coal production, consumption, imports and exports information. The ICSD is a secondary data base, meaning that information contained therein is derived entirely from other primary sources. It uses dBase III+ and Lotus 1-2-3 to locate, report and display data. The system is used for analysis in preparing the Annual Prospects for World Coal Trade (DOE/EIA-0363) publication. The ICSD system is menu driven and also permits the user who is familiar with dBase and Lotus operations to leave the menu structure to perform independent queries. Documentation for the ICSD consists of three manuals -- the User's Guide, the Operations Manual, and the Program Maintenance Manual. This Program Maintenance Manual provides the information necessary to maintain and update the ICSD system. Two major types of program maintenance documentation are presented in this manual. The first is the source code for the dBase III+ routines and related non-dBase programs used in operating the ICSD. The second is listings of the major component database field structures. A third important consideration for dBase programming, the structure of index files, is presented in the listing of source code for the index maintenance program. 1 fig

Network similarity and statistical analysis of earthquake seismic data

OpenAIRE

Deyasi, Krishanu; Chakraborty, Abhijit; Banerjee, Anirban

2016-01-01

We study the structural similarity of earthquake networks constructed from seismic catalogs of different geographical regions. A hierarchical clustering of underlying undirected earthquake networks is shown using Jensen-Shannon divergence in graph spectra. The directed nature of links indicates that each earthquake network is strongly connected, which motivates us to study the directed version statistically. Our statistical analysis of each earthquake region identifies the hub regions. We cal...

Statistical analysis and interpolation of compositional data in materials science.

Science.gov (United States)

Pesenson, Misha Z; Suram, Santosh K; Gregoire, John M

2015-02-09

Compositional data are ubiquitous in chemistry and materials science: analysis of elements in multicomponent systems, combinatorial problems, etc., lead to data that are non-negative and sum to a constant (for example, atomic concentrations). The constant sum constraint restricts the sampling space to a simplex instead of the usual Euclidean space. Since statistical measures such as mean and standard deviation are defined for the Euclidean space, traditional correlation studies, multivariate analysis, and hypothesis testing may lead to erroneous dependencies and incorrect inferences when applied to compositional data. Furthermore, composition measurements that are used for data analytics may not include all of the elements contained in the material; that is, the measurements may be subcompositions of a higher-dimensional parent composition. Physically meaningful statistical analysis must yield results that are invariant under the number of composition elements, requiring the application of specialized statistical tools. We present specifics and subtleties of compositional data processing through discussion of illustrative examples. We introduce basic concepts, terminology, and methods required for the analysis of compositional data and utilize them for the spatial interpolation of composition in a sputtered thin film. The results demonstrate the importance of this mathematical framework for compositional data analysis (CDA) in the fields of materials science and chemistry.

De-trending of wind speed variance based on first-order and second-order statistical moments only

DEFF Research Database (Denmark)

Larsen, Gunner Chr.; Hansen, Kurt Schaldemose

2014-01-01

The lack of efficient methods for de-trending of wind speed resource data may lead to erroneous wind turbine fatigue and ultimate load predictions. The present paper presents two models, which quantify the effect of an assumed linear trend on wind speed standard deviations as based on available...... statistical data only. The first model is a pure time series analysis approach, which quantifies the effect of non-stationary characteristics of ensemble mean wind speeds on the estimated wind speed standard deviations as based on mean wind speed statistics only. This model is applicable to statistics...... of arbitrary types of time series. The second model uses the full set of information and includes thus additionally observed wind speed standard deviations to estimate the effect of ensemble mean non-stationarities on wind speed standard deviations. This model takes advantage of a simple physical relationship...

Evaluating statistical cloud schemes: What can we gain from ground-based remote sensing?

Science.gov (United States)

Grützun, V.; Quaas, J.; Morcrette, C. J.; Ament, F.

2013-09-01

Statistical cloud schemes with prognostic probability distribution functions have become more important in atmospheric modeling, especially since they are in principle scale adaptive and capture cloud physics in more detail. While in theory the schemes have a great potential, their accuracy is still questionable. High-resolution three-dimensional observational data of water vapor and cloud water, which could be used for testing them, are missing. We explore the potential of ground-based remote sensing such as lidar, microwave, and radar to evaluate prognostic distribution moments using the "perfect model approach." This means that we employ a high-resolution weather model as virtual reality and retrieve full three-dimensional atmospheric quantities and virtual ground-based observations. We then use statistics from the virtual observation to validate the modeled 3-D statistics. Since the data are entirely consistent, any discrepancy occurring is due to the method. Focusing on total water mixing ratio, we find that the mean ratio can be evaluated decently but that it strongly depends on the meteorological conditions as to whether the variance and skewness are reliable. Using some simple schematic description of different synoptic conditions, we show how statistics obtained from point or line measurements can be poor at representing the full three-dimensional distribution of water in the atmosphere. We argue that a careful analysis of measurement data and detailed knowledge of the meteorological situation is necessary to judge whether we can use the data for an evaluation of higher moments of the humidity distribution used by a statistical cloud scheme.

Statistical analysis of real ILI (In-Line Inspection) data: implications, inferences and lessons learned

Energy Technology Data Exchange (ETDEWEB)

Timashev, Svyatoslav A.; Bushinskaya, Anna V. [Russian Academy of Sciences, Ekaterinburg (Russian Federation). Ural Branch. Sciences and Engineering Center ' Reliability and Safety of Large Systems and Machines'

2009-07-01

The paper discusses current possibilities and drawbacks of in-line inspection (ILI) in sizing defects in oil and gas pipelines. A methodology based on analysis of variances (ANOVA) is presented that extracts maximum possible information from the ILI measurements of defects and subsequent verification results. This full statistical analysis (FSA) methodology was extensively tested by using the Monte Carlo simulation method. It was then applied to analyze the content of sections 7, 9 and appendix E of the API 1163 RP Standard. (author)

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

Science.gov (United States)

Mathur, Sunil; Sadana, Ajit

2015-12-01

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

Explorations in Statistics: The Analysis of Ratios and Normalized Data

Science.gov (United States)

Curran-Everett, Douglas

2013-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This ninth installment of "Explorations in Statistics" explores the analysis of ratios and normalized--or standardized--data. As researchers, we compute a ratio--a numerator divided by a denominator--to compute a…

Statistical Energy Analysis (SEA) and Energy Finite Element Analysis (EFEA) Predictions for a Floor-Equipped Composite Cylinder

Science.gov (United States)

Grosveld, Ferdinand W.; Schiller, Noah H.; Cabell, Randolph H.

2011-01-01

Comet Enflow is a commercially available, high frequency vibroacoustic analysis software founded on Energy Finite Element Analysis (EFEA) and Energy Boundary Element Analysis (EBEA). Energy Finite Element Analysis (EFEA) was validated on a floor-equipped composite cylinder by comparing EFEA vibroacoustic response predictions with Statistical Energy Analysis (SEA) and experimental results. Statistical Energy Analysis (SEA) predictions were made using the commercial software program VA One 2009 from ESI Group. The frequency region of interest for this study covers the one-third octave bands with center frequencies from 100 Hz to 4000 Hz.

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

Science.gov (United States)

Djorgovski, S. G.

1994-01-01

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

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

Science.gov (United States)

Djorgovski, S. George

1994-01-01

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

Simulation Experiments in Practice : Statistical Design and Regression Analysis

NARCIS (Netherlands)

Kleijnen, J.P.C.

2007-01-01

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. The goal of this article is to change these traditional, naïve methods of design and analysis, because statistical theory proves that more information is

Statistical assignment of DNA sequences using Bayesian phylogenetics

DEFF Research Database (Denmark)

Terkelsen, Kasper Munch; Boomsma, Wouter Krogh; Huelsenbeck, John P.

2008-01-01

We provide a new automated statistical method for DNA barcoding based on a Bayesian phylogenetic analysis. The method is based on automated database sequence retrieval, alignment, and phylogenetic analysis using a custom-built program for Bayesian phylogenetic analysis. We show on real data...... that the method outperforms Blast searches as a measure of confidence and can help eliminate 80% of all false assignment based on best Blast hit. However, the most important advance of the method is that it provides statistically meaningful measures of confidence. We apply the method to a re......-analysis of previously published ancient DNA data and show that, with high statistical confidence, most of the published sequences are in fact of Neanderthal origin. However, there are several cases of chimeric sequences that are comprised of a combination of both Neanderthal and modern human DNA....

Models and statistical analysis of organic micropollutants in groundwater-based drinking water resources

DEFF Research Database (Denmark)

Malaguerra, Flavio

The access to safe drinking water is essential for the well being of the population. The spread of micropollutant contamination jeopardise many freshwater reservoirs, and is a serious threat for human health, especially because of its long-term effects. To asses the threat of contamination, models...... to model. The identification of dominant processes is an essential step in the understanding of system behaviour, because it enables the development of simplified models that can approximate the fate of contaminants with the best trade-off between model complexity and reliability of results. In this thesis......, global sensitivity analysis techniques are used to assess detailed models in order to identify the main processes involved in the degradation of chlorinated solvents in the subsurface, and in the transport of pesticides from surface water into nearby wells in confined aquifers. Statistical techniques...

Uncertainty analysis with statistically correlated failure data

International Nuclear Information System (INIS)

Modarres, M.; Dezfuli, H.; Roush, M.L.

1987-01-01

Likelihood of occurrence of the top event of a fault tree or sequences of an event tree is estimated from the failure probability of components that constitute the events of the fault/event tree. Component failure probabilities are subject to statistical uncertainties. In addition, there are cases where the failure data are statistically correlated. At present most fault tree calculations are based on uncorrelated component failure data. This chapter describes a methodology for assessing the probability intervals for the top event failure probability of fault trees or frequency of occurrence of event tree sequences when event failure data are statistically correlated. To estimate mean and variance of the top event, a second-order system moment method is presented through Taylor series expansion, which provides an alternative to the normally used Monte Carlo method. For cases where component failure probabilities are statistically correlated, the Taylor expansion terms are treated properly. Moment matching technique is used to obtain the probability distribution function of the top event through fitting the Johnson Ssub(B) distribution. The computer program, CORRELATE, was developed to perform the calculations necessary for the implementation of the method developed. (author)

Statistical trend analysis methodology for rare failures in changing technical systems

International Nuclear Information System (INIS)

Ott, K.O.; Hoffmann, H.J.

1983-07-01

A methodology for a statistical trend analysis (STA) in failure rates is presented. It applies primarily to relatively rare events in changing technologies or components. The formulation is more general and the assumptions are less restrictive than in a previously published version. Relations of the statistical analysis and probabilistic assessment (PRA) are discussed in terms of categorization of decisions for action following particular failure events. The significance of tentatively identified trends is explored. In addition to statistical tests for trend significance, a combination of STA and PRA results quantifying the trend complement is proposed. The STA approach is compared with other concepts for trend characterization. (orig.)

Energy statistics yearbook 2002

International Nuclear Information System (INIS)

2005-01-01

The Energy Statistics Yearbook 2002 is a comprehensive collection of international energy statistics prepared by the United Nations Statistics Division. It is the forty-sixth in a series of annual compilations which commenced under the title World Energy Supplies in Selected Years, 1929-1950. It updates the statistical series shown in the previous issue. Supplementary series of monthly and quarterly data on production of energy may be found in the Monthly Bulletin of Statistics. The principal objective of the Yearbook is to provide a global framework of comparable data on long-term trends in the supply of mainly commercial primary and secondary forms of energy. Data for each type of fuel and aggregate data for the total mix of commercial fuels are shown for individual countries and areas and are summarized into regional and world totals. The data are compiled primarily from the annual energy questionnaire distributed by the United Nations Statistics Division and supplemented by official national statistical publications. Where official data are not available or are inconsistent, estimates are made by the Statistics Division based on governmental, professional or commercial materials. Estimates include, but are not limited to, extrapolated data based on partial year information, use of annual trends, trade data based on partner country reports, breakdowns of aggregated data as well as analysis of current energy events and activities

Energy statistics yearbook 2001

International Nuclear Information System (INIS)

2004-01-01

The Energy Statistics Yearbook 2001 is a comprehensive collection of international energy statistics prepared by the United Nations Statistics Division. It is the forty-fifth in a series of annual compilations which commenced under the title World Energy Supplies in Selected Years, 1929-1950. It updates the statistical series shown in the previous issue. Supplementary series of monthly and quarterly data on production of energy may be found in the Monthly Bulletin of Statistics. The principal objective of the Yearbook is to provide a global framework of comparable data on long-term trends in the supply of mainly commercial primary and secondary forms of energy. Data for each type of fuel and aggregate data for the total mix of commercial fuels are shown for individual countries and areas and are summarized into regional and world totals. The data are compiled primarily from the annual energy questionnaire distributed by the United Nations Statistics Division and supplemented by official national statistical publications. Where official data are not available or are inconsistent, estimates are made by the Statistics Division based on governmental, professional or commercial materials. Estimates include, but are not limited to, extrapolated data based on partial year information, use of annual trends, trade data based on partner country reports, breakdowns of aggregated data as well as analysis of current energy events and activities

Energy statistics yearbook 2000

International Nuclear Information System (INIS)

2002-01-01

The Energy Statistics Yearbook 2000 is a comprehensive collection of international energy statistics prepared by the United Nations Statistics Division. It is the forty-third in a series of annual compilations which commenced under the title World Energy Supplies in Selected Years, 1929-1950. It updates the statistical series shown in the previous issue. Supplementary series of monthly and quarterly data on production of energy may be found in the Monthly Bulletin of Statistics. The principal objective of the Yearbook is to provide a global framework of comparable data on long-term trends in the supply of mainly commercial primary and secondary forms of energy. Data for each type of fuel and aggregate data for the total mix of commercial fuels are shown for individual countries and areas and are summarized into regional and world totals. The data are compiled primarily from the annual energy questionnaire distributed by the United Nations Statistics Division and supplemented by official national statistical publications. Where official data are not available or are inconsistent, estimates are made by the Statistics Division based on governmental, professional or commercial materials. Estimates include, but are not limited to, extrapolated data based on partial year information, use of annual trends, trade data based on partner country reports, breakdowns of aggregated data as well as analysis of current energy events and activities

The null hypothesis of GSEA, and a novel statistical model for competitive gene set analysis

DEFF Research Database (Denmark)

Debrabant, Birgit

2017-01-01

MOTIVATION: Competitive gene set analysis intends to assess whether a specific set of genes is more associated with a trait than the remaining genes. However, the statistical models assumed to date to underly these methods do not enable a clear cut formulation of the competitive null hypothesis....... This is a major handicap to the interpretation of results obtained from a gene set analysis. RESULTS: This work presents a hierarchical statistical model based on the notion of dependence measures, which overcomes this problem. The two levels of the model naturally reflect the modular structure of many gene set...... analysis methods. We apply the model to show that the popular GSEA method, which recently has been claimed to test the self-contained null hypothesis, actually tests the competitive null if the weight parameter is zero. However, for this result to hold strictly, the choice of the dependence measures...

Analysis of thrips distribution: application of spatial statistics and Kriging

Science.gov (United States)

John Aleong; Bruce L. Parker; Margaret Skinner; Diantha Howard

1991-01-01

Kriging is a statistical technique that provides predictions for spatially and temporally correlated data. Observations of thrips distribution and density in Vermont soils are made in both space and time. Traditional statistical analysis of such data assumes that the counts taken over space and time are independent, which is not necessarily true. Therefore, to analyze...

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

Science.gov (United States)

Sayemuzzaman, M.; Ye, M.

2015-12-01

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

An analysis of distribution transformer failure using the statistical package for the social sciences (SPSS software

Directory of Open Access Journals (Sweden)

María Gabriela Mago Ramos

2012-05-01

Full Text Available A methodology was developed for analysing faults in distribution transformers using the statistical package for social sciences (SPSS; it consisted of organising and creating of database regarding failed equipment, incorporating such data into the processing programme and converting all the information into numerical variables to be processed, thereby obtaining descriptive statistics and enabling factor and discriminant analysis. The research was based on information provided by companies in areas served by Corpoelec (Valencia, Venezuela and Codensa (Bogotá, Colombia.

Net analyte signal based statistical quality control

NARCIS (Netherlands)

Skibsted, E.T.S.; Boelens, H.F.M.; Westerhuis, J.A.; Smilde, A.K.; Broad, N.W.; Rees, D.R.; Witte, D.T.

2005-01-01

Net analyte signal statistical quality control (NAS-SQC) is a new methodology to perform multivariate product quality monitoring based on the net analyte signal approach. The main advantage of NAS-SQC is that the systematic variation in the product due to the analyte (or property) of interest is

Statistical state dynamics-based analysis of the physical mechanisms sustaining and regulating turbulence in Couette flow

Science.gov (United States)

Farrell, Brian F.; Ioannou, Petros J.

2017-08-01

This paper describes a study of the self-sustaining process in wall turbulence. The study is based on a second order statistical state dynamics model of Couette flow in which the state variables are the streamwise mean flow (first cumulant) and perturbation covariance (second cumulant). This statistical state dynamics model is closed by either setting the third cumulant to zero or by replacing it with a stochastic parametrization. Statistical state dynamics models with this form are referred to as S3T models. S3T models have been shown to self-sustain turbulence with a mean flow and second order perturbation structure similar to that obtained by direct numerical simulation of the equations of motion. The use of a statistical state dynamics model to study the physical mechanisms underlying turbulence has important advantages over the traditional approach of studying the dynamics of individual realizations of turbulence. One advantage is that the analytical structure of S3T statistical state dynamics models isolates the interaction between the mean flow and the perturbation components of the turbulence. Isolation of the interaction between these components reveals how this interaction underlies both the maintenance of the turbulence variance by transfer of energy from the externally driven flow to the perturbation components as well as the enforcement of the observed statistical mean turbulent state by feedback regulation between the mean and perturbation fields. Another advantage of studying turbulence using statistical state dynamics models of S3T form is that the analytical structure of S3T turbulence can be completely characterized. For example, the perturbation component of turbulence in the S3T system is demonstrably maintained by a parametric perturbation growth mechanism in which fluctuation of the mean flow maintains the perturbation field which in turn maintains the mean flow fluctuations in a synergistic interaction. Furthermore, the equilibrium

Statistical analysis of the Ft. Calhoun reactor coolant pump system

International Nuclear Information System (INIS)

Heising, Carolyn D.

1998-01-01

In engineering science, statistical quality control techniques have traditionally been applied to control manufacturing processes. An application to commercial nuclear power plant maintenance and control is presented that can greatly improve plant safety. As a demonstration of such an approach to plant maintenance and control, a specific system is analyzed: the reactor coolant pumps (RCPs) of the Ft. Calhoun nuclear power plant. This research uses capability analysis, Shewhart X-bar, R-charts, canonical correlation methods, and design of experiments to analyze the process for the state of statistical control. The results obtained show that six out of ten parameters are under control specifications limits and four parameters are not in the state of statistical control. The analysis shows that statistical process control methods can be applied as an early warning system capable of identifying significant equipment problems well in advance of traditional control room alarm indicators Such a system would provide operators with ample time to respond to possible emergency situations and thus improve plant safety and reliability. (author)

Leads Detection Using Mixture Statistical Distribution Based CRF Algorithm from Sentinel-1 Dual Polarization SAR Imagery

Science.gov (United States)

Zhang, Yu; Li, Fei; Zhang, Shengkai; Zhu, Tingting

2017-04-01

Synthetic Aperture Radar (SAR) is significantly important for polar remote sensing since it can provide continuous observations in all days and all weather. SAR can be used for extracting the surface roughness information characterized by the variance of dielectric properties and different polarization channels, which make it possible to observe different ice types and surface structure for deformation analysis. In November, 2016, Chinese National Antarctic Research Expedition (CHINARE) 33rd cruise has set sails in sea ice zone in Antarctic. Accurate leads spatial distribution in sea ice zone for routine planning of ship navigation is essential. In this study, the semantic relationship between leads and sea ice categories has been described by the Conditional Random Fields (CRF) model, and leads characteristics have been modeled by statistical distributions in SAR imagery. In the proposed algorithm, a mixture statistical distribution based CRF is developed by considering the contexture information and the statistical characteristics of sea ice for improving leads detection in Sentinel-1A dual polarization SAR imagery. The unary potential and pairwise potential in CRF model is constructed by integrating the posteriori probability estimated from statistical distributions. For mixture statistical distribution parameter estimation, Method of Logarithmic Cumulants (MoLC) is exploited for single statistical distribution parameters estimation. The iteration based Expectation Maximal (EM) algorithm is investigated to calculate the parameters in mixture statistical distribution based CRF model. In the posteriori probability inference, graph-cut energy minimization method is adopted in the initial leads detection. The post-processing procedures including aspect ratio constrain and spatial smoothing approaches are utilized to improve the visual result. The proposed method is validated on Sentinel-1A SAR C-band Extra Wide Swath (EW) Ground Range Detected (GRD) imagery with a

Statistical Analysis of Bending Rigidity Coefficient Determined Using Fluorescence-Based Flicker-Noise Spectroscopy.

Science.gov (United States)

Doskocz, Joanna; Drabik, Dominik; Chodaczek, Grzegorz; Przybyło, Magdalena; Langner, Marek

2018-06-01

Bending rigidity coefficient describes propensity of a lipid bilayer to deform. In order to measure the parameter experimentally using flickering noise spectroscopy, the microscopic imaging is required, which necessitates the application of giant unilamellar vesicles (GUV) lipid bilayer model. The major difficulty associated with the application of the model is the statistical character of GUV population with respect to their size and the homogeneity of lipid bilayer composition, if a mixture of lipids is used. In the paper, the bending rigidity coefficient was measured using the fluorescence-enhanced flicker-noise spectroscopy. In the paper, the bending rigidity coefficient was determined for large populations of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine and 1,2-dioleoyl-sn-glycero-3-phosphocholine vesicles. The quantity of obtained experimental data allows to perform statistical analysis aiming at the identification of the distribution, which is the most appropriate for the calculation of the value of the membrane bending rigidity coefficient. It has been demonstrated that the bending rigidity coefficient is characterized by an asymmetrical distribution, which is well approximated with the gamma distribution. Since there are no biophysical reasons for that we propose to use the difference between normal and gamma fits as a measure of the homogeneity of vesicle population. In addition, the effect of a fluorescent label and types of instrumental setups on determined values has been tested. Obtained results show that the value of the bending rigidity coefficient does not depend on the type of a fluorescent label nor on the type of microscope used.

Propensity Score Analysis: An Alternative Statistical Approach for HRD Researchers

Science.gov (United States)

Keiffer, Greggory L.; Lane, Forrest C.

2016-01-01

Purpose: This paper aims to introduce matching in propensity score analysis (PSA) as an alternative statistical approach for researchers looking to make causal inferences using intact groups. Design/methodology/approach: An illustrative example demonstrated the varying results of analysis of variance, analysis of covariance and PSA on a heuristic…

Simulation Experiments in Practice: Statistical Design and Regression Analysis

OpenAIRE

Kleijnen, J.P.C.

2007-01-01

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. The goal of this article is to change these traditional, naïve methods of design and analysis, because statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic DOE and regression analysis assume a single simulation response that is normally and independen...

Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies.

Science.gov (United States)

Boulesteix, Anne-Laure; Wilson, Rory; Hapfelmeier, Alexander

2017-09-09

The goal of medical research is to develop interventions that are in some sense superior, with respect to patient outcome, to interventions currently in use. Similarly, the goal of research in methodological computational statistics is to develop data analysis tools that are themselves superior to the existing tools. The methodology of the evaluation of medical interventions continues to be discussed extensively in the literature and it is now well accepted that medicine should be at least partly "evidence-based". Although we statisticians are convinced of the importance of unbiased, well-thought-out study designs and evidence-based approaches in the context of clinical research, we tend to ignore these principles when designing our own studies for evaluating statistical methods in the context of our methodological research. In this paper, we draw an analogy between clinical trials and real-data-based benchmarking experiments in methodological statistical science, with datasets playing the role of patients and methods playing the role of medical interventions. Through this analogy, we suggest directions for improvement in the design and interpretation of studies which use real data to evaluate statistical methods, in particular with respect to dataset inclusion criteria and the reduction of various forms of bias. More generally, we discuss the concept of "evidence-based" statistical research, its limitations and its impact on the design and interpretation of real-data-based benchmark experiments. We suggest that benchmark studies-a method of assessment of statistical methods using real-world datasets-might benefit from adopting (some) concepts from evidence-based medicine towards the goal of more evidence-based statistical research.

Statistical analysis of thermal conductivity of nanofluid containing ...

Indian Academy of Sciences (India)

Thermal conductivity measurements of nanofluids were analysed via two-factor completely randomized design and comparison of data means is carried out with Duncan's multiple-range test. Statistical analysis of experimental data show that temperature and weight fraction have a reasonable impact on the thermal ...

How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.

Science.gov (United States)

Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall

2016-01-01

Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

A PLSPM-Based Test Statistic for Detecting Gene-Gene Co-Association in Genome-Wide Association Study with Case-Control Design

Science.gov (United States)

Zhang, Xiaoshuai; Yang, Xiaowei; Yuan, Zhongshang; Liu, Yanxun; Li, Fangyu; Peng, Bin; Zhu, Dianwen; Zhao, Jinghua; Xue, Fuzhong

2013-01-01

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods. PMID:23620809

Longitudinal data analysis a handbook of modern statistical methods

CERN Document Server

Fitzmaurice, Garrett; Verbeke, Geert; Molenberghs, Geert

2008-01-01

Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory and applications. It also focuses on the assorted challenges that arise in analyzing longitudinal data. After discussing historical aspects, leading researchers explore four broad themes: parametric modeling, nonparametric and semiparametric methods, joint

Mathematical statistics

CERN Document Server

Pestman, Wiebe R

2009-01-01

This textbook provides a broad and solid introduction to mathematical statistics, including the classical subjects hypothesis testing, normal regression analysis, and normal analysis of variance. In addition, non-parametric statistics and vectorial statistics are considered, as well as applications of stochastic analysis in modern statistics, e.g., Kolmogorov-Smirnov testing, smoothing techniques, robustness and density estimation. For students with some elementary mathematical background. With many exercises. Prerequisites from measure theory and linear algebra are presented.

Bayesian Sensitivity Analysis of Statistical Models with Missing Data.

Science.gov (United States)

Zhu, Hongtu; Ibrahim, Joseph G; Tang, Niansheng

2014-04-01

Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures.

Quantitative analysis and IBM SPSS statistics a guide for business and finance

CERN Document Server

Aljandali, Abdulkader

2016-01-01

This guide is for practicing statisticians and data scientists who use IBM SPSS for statistical analysis of big data in business and finance. This is the first of a two-part guide to SPSS for Windows, introducing data entry into SPSS, along with elementary statistical and graphical methods for summarizing and presenting data. Part I also covers the rudiments of hypothesis testing and business forecasting while Part II will present multivariate statistical methods, more advanced forecasting methods, and multivariate methods. IBM SPSS Statistics offers a powerful set of statistical and information analysis systems that run on a wide variety of personal computers. The software is built around routines that have been developed, tested, and widely used for more than 20 years. As such, IBM SPSS Statistics is extensively used in industry, commerce, banking, local and national governments, and education. Just a small subset of users of the package include the major clearing banks, the BBC, British Gas, British Airway...

Drug safety data mining with a tree-based scan statistic.

Science.gov (United States)

Kulldorff, Martin; Dashevsky, Inna; Avery, Taliser R; Chan, Arnold K; Davis, Robert L; Graham, David; Platt, Richard; Andrade, Susan E; Boudreau, Denise; Gunter, Margaret J; Herrinton, Lisa J; Pawloski, Pamala A; Raebel, Marsha A; Roblin, Douglas; Brown, Jeffrey S

2013-05-01

In post-marketing drug safety surveillance, data mining can potentially detect rare but serious adverse events. Assessing an entire collection of drug-event pairs is traditionally performed on a predefined level of granularity. It is unknown a priori whether a drug causes a very specific or a set of related adverse events, such as mitral valve disorders, all valve disorders, or different types of heart disease. This methodological paper evaluates the tree-based scan statistic data mining method to enhance drug safety surveillance. We use a three-million-member electronic health records database from the HMO Research Network. Using the tree-based scan statistic, we assess the safety of selected antifungal and diabetes drugs, simultaneously evaluating overlapping diagnosis groups at different granularity levels, adjusting for multiple testing. Expected and observed adverse event counts were adjusted for age, sex, and health plan, producing a log likelihood ratio test statistic. Out of 732 evaluated disease groupings, 24 were statistically significant, divided among 10 non-overlapping disease categories. Five of the 10 signals are known adverse effects, four are likely due to confounding by indication, while one may warrant further investigation. The tree-based scan statistic can be successfully applied as a data mining tool in drug safety surveillance using observational data. The total number of statistical signals was modest and does not imply a causal relationship. Rather, data mining results should be used to generate candidate drug-event pairs for rigorous epidemiological studies to evaluate the individual and comparative safety profiles of drugs. Copyright © 2013 John Wiley & Sons, Ltd.

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

International Nuclear Information System (INIS)

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

2011-01-01

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

GIS-based spatial statistical analysis of risk areas for liver flukes in Surin Province of Thailand.

Science.gov (United States)

Rujirakul, Ratana; Ueng-arporn, Naporn; Kaewpitoon, Soraya; Loyd, Ryan J; Kaewthani, Sarochinee; Kaewpitoon, Natthawut

2015-01-01

It is urgently necessary to be aware of the distribution and risk areas of liver fluke, Opisthorchis viverrini, for proper allocation of prevention and control measures. This study aimed to investigate the human behavior, and environmental factors influencing the distribution in Surin Province of Thailand, and to build a model using stepwise multiple regression analysis with a geographic information system (GIS) on environment and climate data. The relationship between the human behavior, attitudes (R Square=0.878, and, Adjust R Square=0.849. By GIS analysis, we found Si Narong, Sangkha, Phanom Dong Rak, Mueang Surin, Non Narai, Samrong Thap, Chumphon Buri, and Rattanaburi to have the highest distributions in Surin province. In conclusion, the combination of GIS and statistical analysis can help simulate the spatial distribution and risk areas of liver fluke, and thus may be an important tool for future planning of prevention and control measures.

Spectral signature verification using statistical analysis and text mining

Science.gov (United States)

DeCoster, Mallory E.; Firpi, Alexe H.; Jacobs, Samantha K.; Cone, Shelli R.; Tzeng, Nigel H.; Rodriguez, Benjamin M.

2016-05-01

In the spectral science community, numerous spectral signatures are stored in databases representative of many sample materials collected from a variety of spectrometers and spectroscopists. Due to the variety and variability of the spectra that comprise many spectral databases, it is necessary to establish a metric for validating the quality of spectral signatures. This has been an area of great discussion and debate in the spectral science community. This paper discusses a method that independently validates two different aspects of a spectral signature to arrive at a final qualitative assessment; the textual meta-data and numerical spectral data. Results associated with the spectral data stored in the Signature Database1 (SigDB) are proposed. The numerical data comprising a sample material's spectrum is validated based on statistical properties derived from an ideal population set. The quality of the test spectrum is ranked based on a spectral angle mapper (SAM) comparison to the mean spectrum derived from the population set. Additionally, the contextual data of a test spectrum is qualitatively analyzed using lexical analysis text mining. This technique analyzes to understand the syntax of the meta-data to provide local learning patterns and trends within the spectral data, indicative of the test spectrum's quality. Text mining applications have successfully been implemented for security2 (text encryption/decryption), biomedical3 , and marketing4 applications. The text mining lexical analysis algorithm is trained on the meta-data patterns of a subset of high and low quality spectra, in order to have a model to apply to the entire SigDB data set. The statistical and textual methods combine to assess the quality of a test spectrum existing in a database without the need of an expert user. This method has been compared to other validation methods accepted by the spectral science community, and has provided promising results when a baseline spectral signature is

Statistical Analysis of Detailed 3-D CFD LES Simulations with Regard to CCV Modeling

Directory of Open Access Journals (Sweden)

Vítek Oldřich

2016-06-01

Full Text Available The paper deals with statistical analysis of large amount of detailed 3-D CFD data in terms of cycle-to-cycle variations (CCVs. These data were obtained by means of LES calculations of many consecutive cycles. Due to non-linear nature of Navier-Stokes equation set, there is a relatively significant CCV. Hence, every cycle is slightly different – this leads to requirement to perform statistical analysis based on ensemble averaging procedure which enables better understanding of CCV in ICE including its quantification. The data obtained from the averaging procedure provides results on different space resolution levels. The procedure is applied locally, i.e., in every cell of the mesh. Hence there is detailed CCV information on local level – such information can be compared with RANS simulations. Next, volume/mass averaging provides information at specific locations – e.g., gap between electrodes of a spark plug. Finally, volume/mass averaging of the whole combustion chamber leads to global information which can be compared with experimental data or results of system simulation tools (which are based on 0-D/1-D approach.

Computerized statistical analysis with bootstrap method in nuclear medicine

International Nuclear Information System (INIS)

Zoccarato, O.; Sardina, M.; Zatta, G.; De Agostini, A.; Barbesti, S.; Mana, O.; Tarolo, G.L.

1988-01-01

Statistical analysis of data samples involves some hypothesis about the features of data themselves. The accuracy of these hypotheses can influence the results of statistical inference. Among the new methods of computer-aided statistical analysis, the bootstrap method appears to be one of the most powerful, thanks to its ability to reproduce many artificial samples starting from a single original sample and because it works without hypothesis about data distribution. The authors applied the bootstrap method to two typical situation of Nuclear Medicine Department. The determination of the normal range of serum ferritin, as assessed by radioimmunoassay and defined by the mean value ±2 standard deviations, starting from an experimental sample of small dimension, shows an unacceptable lower limit (ferritin plasmatic levels below zero). On the contrary, the results obtained by elaborating 5000 bootstrap samples gives ans interval of values (10.95 ng/ml - 72.87 ng/ml) corresponding to the normal ranges commonly reported. Moreover the authors applied the bootstrap method in evaluating the possible error associated with the correlation coefficient determined between left ventricular ejection fraction (LVEF) values obtained by first pass radionuclide angiocardiography with 99m Tc and 195m Au. The results obtained indicate a high degree of statistical correlation and give the range of r 2 values to be considered acceptable for this type of studies

PRECISE - pregabalin in addition to usual care: Statistical analysis plan

NARCIS (Netherlands)

S. Mathieson (Stephanie); L. Billot (Laurent); C. Maher (Chris); A.J. McLachlan (Andrew J.); J. Latimer (Jane); B.W. Koes (Bart); M.J. Hancock (Mark J.); I. Harris (Ian); R.O. Day (Richard O.); J. Pik (Justin); S. Jan (Stephen); C.-W.C. Lin (Chung-Wei Christine)

2016-01-01

textabstractBackground: Sciatica is a severe, disabling condition that lacks high quality evidence for effective treatment strategies. This a priori statistical analysis plan describes the methodology of analysis for the PRECISE study. Methods/design: PRECISE is a prospectively registered, double

Development and Assessment of a Preliminary Randomization-Based Introductory Statistics Curriculum

Science.gov (United States)

Tintle, Nathan; VanderStoep, Jill; Holmes, Vicki-Lynn; Quisenberry, Brooke; Swanson, Todd

2011-01-01

The algebra-based introductory statistics course is the most popular undergraduate course in statistics. While there is a general consensus for the content of the curriculum, the recent Guidelines for Assessment and Instruction in Statistics Education (GAISE) have challenged the pedagogy of this course. Additionally, some arguments have been made…

Statistical analysis of dimer formation in supersaturated metal vapor based on molecular dynamics simulation

Science.gov (United States)

Korenchenko, Anna E.; Vorontsov, Alexander G.; Gelchinski, Boris R.; Sannikov, Grigorii P.

2018-04-01

We discuss the problem of dimer formation during the homogeneous nucleation of atomic metal vapor in an inert gas environment. We simulated nucleation with molecular dynamics and carried out the statistical analysis of double- and triple-atomic collisions as the two ways of long-lived diatomic complex formation. Close pair of atoms with lifetime greater than the mean time interval between atom-atom collisions is called a long-lived diatomic complex. We found that double- and triple-atomic collisions gave approximately the same probabilities of long-lived diatomic complex formation, but internal energy of the resulted state was essentially lower in the second case. Some diatomic complexes formed in three-particle collisions are stable enough to be a critical nucleus.

Multivariate statistical analysis of atom probe tomography data

International Nuclear Information System (INIS)

Parish, Chad M.; Miller, Michael K.

2010-01-01

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

Statistical analysis of accident data associated with sea transport (invited paper)

International Nuclear Information System (INIS)

Raffestin, D.; Armingaud, F.; Schneider, T.; Delaigue, S.

1998-01-01

This analysis, based on Lloyd's database, gives an accurate description of the world fleet and the most severe ship accidents, as well as the frequencies of accident per ship type, accident category and age category. Complementary analyses were achieved using fire accident databases from AEA Technology and the French Bureau Veritas. The results should be used in the perspective of safety assessments of maritime shipments of radioactive material. For this purpose the existence of the regulations of the International Maritime Organisation has to be considered, leading to the introduction of correction factors to these statistical data derived from general cargo-carrying ships. (author)

On exploiting wavelet bases in statistical region-based segmentation

DEFF Research Database (Denmark)

Stegmann, Mikkel Bille; Forchhammer, Søren

2002-01-01

Statistical region-based segmentation methods such as the Active Appearance Models establish dense correspondences by modelling variation of shape and pixel intensities in low-resolution 2D images. Unfortunately, for high-resolution 2D and 3D images, this approach is rendered infeasible due to ex...... 9-7 wavelet on cardiac MRIs and human faces show that the segmentation accuracy is minimally degraded at compression ratios of 1:10 and 1:20, respectively....

CORSSA: Community Online Resource for Statistical Seismicity Analysis

Science.gov (United States)

Zechar, J. D.; Hardebeck, J. L.; Michael, A. J.; Naylor, M.; Steacy, S.; Wiemer, S.; Zhuang, J.

2011-12-01

Statistical seismology is critical to the understanding of seismicity, the evaluation of proposed earthquake prediction and forecasting methods, and the assessment of seismic hazard. Unfortunately, despite its importance to seismology-especially to those aspects with great impact on public policy-statistical seismology is mostly ignored in the education of seismologists, and there is no central repository for the existing open-source software tools. To remedy these deficiencies, and with the broader goal to enhance the quality of statistical seismology research, we have begun building the Community Online Resource for Statistical Seismicity Analysis (CORSSA, www.corssa.org). We anticipate that the users of CORSSA will range from beginning graduate students to experienced researchers. More than 20 scientists from around the world met for a week in Zurich in May 2010 to kick-start the creation of CORSSA: the format and initial table of contents were defined; a governing structure was organized; and workshop participants began drafting articles. CORSSA materials are organized with respect to six themes, each will contain between four and eight articles. CORSSA now includes seven articles with an additional six in draft form along with forums for discussion, a glossary, and news about upcoming meetings, special issues, and recent papers. Each article is peer-reviewed and presents a balanced discussion, including illustrative examples and code snippets. Topics in the initial set of articles include: introductions to both CORSSA and statistical seismology, basic statistical tests and their role in seismology; understanding seismicity catalogs and their problems; basic techniques for modeling seismicity; and methods for testing earthquake predictability hypotheses. We have also begun curating a collection of statistical seismology software packages.

Cognitive Transfer Outcomes for a Simulation-Based Introductory Statistics Curriculum

Science.gov (United States)

Backman, Matthew D.; Delmas, Robert C.; Garfield, Joan

2017-01-01

Cognitive transfer is the ability to apply learned skills and knowledge to new applications and contexts. This investigation evaluates cognitive transfer outcomes for a tertiary-level introductory statistics course using the CATALST curriculum, which exclusively used simulation-based methods to develop foundations of statistical inference. A…

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

International Nuclear Information System (INIS)

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

2014-01-01

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

Data base of accident and agricultural statistics for transportation risk assessment

Energy Technology Data Exchange (ETDEWEB)

Saricks, C.L.; Williams, R.G.; Hopf, M.R.

1989-11-01

A state-level data base of accident and agricultural statistics has been developed to support risk assessment for transportation of spent nuclear fuels and high-level radioactive wastes. This data base will enhance the modeling capabilities for more route-specific analyses of potential risks associated with transportation of these wastes to a disposal site. The data base and methodology used to develop state-specific accident and agricultural data bases are described, and summaries of accident and agricultural statistics are provided. 27 refs., 9 tabs.

Data base of accident and agricultural statistics for transportation risk assessment

International Nuclear Information System (INIS)

Saricks, C.L.; Williams, R.G.; Hopf, M.R.

1989-11-01

A state-level data base of accident and agricultural statistics has been developed to support risk assessment for transportation of spent nuclear fuels and high-level radioactive wastes. This data base will enhance the modeling capabilities for more route-specific analyses of potential risks associated with transportation of these wastes to a disposal site. The data base and methodology used to develop state-specific accident and agricultural data bases are described, and summaries of accident and agricultural statistics are provided. 27 refs., 9 tabs

EVOLUTION OF ECONOMY AND ITS IMPACT ON INSURANCE MARKET – A STATISTICAL ANALYSIS

Directory of Open Access Journals (Sweden)

Sandra TEODORESCU

2015-07-01

Full Text Available The economic context has a strong impact on the insurance sector. On the one hand, the decisions related to sector regulation could influence the life of the insurance companies. On the other hand, taxes and other measures that may affect purchasing power, economic instability represent a threat. It is a fact that insurances are products that customers and companies access them when economic conditions are predictable and budgets could be accurately predicted. The paper mainly analyzes in terms of indicators, the impact of the economy on the insurance sector. We are talking about the interrelationships between Gross Written Premiums (the insurance "Budget" and some macroeconomic indicators characterizing the Romanian economy, such as Gross Domestic Product, net average earnings, the average number of employees etc. Statistical analysis is performed for a 12-year period during 2002-2013. This analysis is based on official statistics published by the National Institute of Statistics, the National Forecasting Commission and the National Bank of Romania. The methodology consists of correlation and regression analysis. Of the variables used in the study we mention: Gross Domestic Product, the number of employees, employment, net average earnings, the activity rate of the working age population (15-64 years, non-governmental domestic credit. The analysis of correlation between the studied variables reveals that is a strong correlation between Gross Written Premiums and GDP, the number of employees, average earnings and non-government domestic credit.. Thus, economic growth, rising incomes, the increasing number of employees and facilitating credit conditions could be some elements that would lead to sustainable growth of the insurance market.

Visual and statistical analysis of 18F-FDG PET in primary progressive aphasia

International Nuclear Information System (INIS)

Matias-Guiu, Jordi A.; Moreno-Ramos, Teresa; Garcia-Ramos, Rocio; Fernandez-Matarrubia, Marta; Oreja-Guevara, Celia; Matias-Guiu, Jorge; Cabrera-Martin, Maria Nieves; Perez-Castejon, Maria Jesus; Rodriguez-Rey, Cristina; Ortega-Candil, Aida; Carreras, Jose Luis

2015-01-01

Diagnosing progressive primary aphasia (PPA) and its variants is of great clinical importance, and fluorodeoxyglucose (FDG) positron emission tomography (PET) may be a useful diagnostic technique. The purpose of this study was to evaluate interobserver variability in the interpretation of FDG PET images in PPA as well as the diagnostic sensitivity and specificity of the technique. We also aimed to compare visual and statistical analyses of these images. There were 10 raters who analysed 44 FDG PET scans from 33 PPA patients and 11 controls. Five raters analysed the images visually, while the other five used maps created using Statistical Parametric Mapping software. Two spatial normalization procedures were performed: global mean normalization and cerebellar normalization. Clinical diagnosis was considered the gold standard. Inter-rater concordance was moderate for visual analysis (Fleiss' kappa 0.568) and substantial for statistical analysis (kappa 0.756-0.881). Agreement was good for all three variants of PPA except for the nonfluent/agrammatic variant studied with visual analysis. The sensitivity and specificity of each rater's diagnosis of PPA was high, averaging 87.8 and 89.9 % for visual analysis and 96.9 and 90.9 % for statistical analysis using global mean normalization, respectively. In cerebellar normalization, sensitivity was 88.9 % and specificity 100 %. FDG PET demonstrated high diagnostic accuracy for the diagnosis of PPA and its variants. Inter-rater concordance was higher for statistical analysis, especially for the nonfluent/agrammatic variant. These data support the use of FDG PET to evaluate patients with PPA and show that statistical analysis methods are particularly useful for identifying the nonfluent/agrammatic variant of PPA. (orig.)

Composition-based statistics and translated nucleotide searches: Improving the TBLASTN module of BLAST

Directory of Open Access Journals (Sweden)

Schäffer Alejandro A

2006-12-01

Full Text Available Abstract Background TBLASTN is a mode of operation for BLAST that aligns protein sequences to a nucleotide database translated in all six frames. We present the first description of the modern implementation of TBLASTN, focusing on new techniques that were used to implement composition-based statistics for translated nucleotide searches. Composition-based statistics use the composition of the sequences being aligned to generate more accurate E-values, which allows for a more accurate distinction between true and false matches. Until recently, composition-based statistics were available only for protein-protein searches. They are now available as a command line option for recent versions of TBLASTN and as an option for TBLASTN on the NCBI BLAST web server. Results We evaluate the statistical and retrieval accuracy of the E-values reported by a baseline version of TBLASTN and by two variants that use different types of composition-based statistics. To test the statistical accuracy of TBLASTN, we ran 1000 searches using scrambled proteins from the mouse genome and a database of human chromosomes. To test retrieval accuracy, we modernize and adapt to translated searches a test set previously used to evaluate the retrieval accuracy of protein-protein searches. We show that composition-based statistics greatly improve the statistical accuracy of TBLASTN, at a small cost to the retrieval accuracy. Conclusion TBLASTN is widely used, as it is common to wish to compare proteins to chromosomes or to libraries of mRNAs. Composition-based statistics improve the statistical accuracy, and therefore the reliability, of TBLASTN results. The algorithms used by TBLASTN are not widely known, and some of the most important are reported here. The data used to test TBLASTN are available for download and may be useful in other studies of translated search algorithms.

Australasian Resuscitation In Sepsis Evaluation trial statistical analysis plan.

Science.gov (United States)

Delaney, Anthony; Peake, Sandra L; Bellomo, Rinaldo; Cameron, Peter; Holdgate, Anna; Howe, Belinda; Higgins, Alisa; Presneill, Jeffrey; Webb, Steve

2013-10-01

The Australasian Resuscitation In Sepsis Evaluation (ARISE) study is an international, multicentre, randomised, controlled trial designed to evaluate the effectiveness of early goal-directed therapy compared with standard care for patients presenting to the ED with severe sepsis. In keeping with current practice, and taking into considerations aspects of trial design and reporting specific to non-pharmacologic interventions, this document outlines the principles and methods for analysing and reporting the trial results. The document is prepared prior to completion of recruitment into the ARISE study, without knowledge of the results of the interim analysis conducted by the data safety and monitoring committee and prior to completion of the two related international studies. The statistical analysis plan was designed by the ARISE chief investigators, and reviewed and approved by the ARISE steering committee. The data collected by the research team as specified in the study protocol, and detailed in the study case report form were reviewed. Information related to baseline characteristics, characteristics of delivery of the trial interventions, details of resuscitation and other related therapies, and other relevant data are described with appropriate comparisons between groups. The primary, secondary and tertiary outcomes for the study are defined, with description of the planned statistical analyses. A statistical analysis plan was developed, along with a trial profile, mock-up tables and figures. A plan for presenting baseline characteristics, microbiological and antibiotic therapy, details of the interventions, processes of care and concomitant therapies, along with adverse events are described. The primary, secondary and tertiary outcomes are described along with identification of subgroups to be analysed. A statistical analysis plan for the ARISE study has been developed, and is available in the public domain, prior to the completion of recruitment into the

The skeletal maturation status estimated by statistical shape analysis: axial images of Japanese cervical vertebra.

Science.gov (United States)

Shin, S M; Kim, Y-I; Choi, Y-S; Yamaguchi, T; Maki, K; Cho, B-H; Park, S-B

2015-01-01

To evaluate axial cervical vertebral (ACV) shape quantitatively and to build a prediction model for skeletal maturation level using statistical shape analysis for Japanese individuals. The sample included 24 female and 19 male patients with hand-wrist radiographs and CBCT images. Through generalized Procrustes analysis and principal components (PCs) analysis, the meaningful PCs were extracted from each ACV shape and analysed for the estimation regression model. Each ACV shape had meaningful PCs, except for the second axial cervical vertebra. Based on these models, the smallest prediction intervals (PIs) were from the combination of the shape space PCs, age and gender. Overall, the PIs of the male group were smaller than those of the female group. There was no significant correlation between centroid size as a size factor and skeletal maturation level. Our findings suggest that the ACV maturation method, which was applied by statistical shape analysis, could confirm information about skeletal maturation in Japanese individuals as an available quantifier of skeletal maturation and could be as useful a quantitative method as the skeletal maturation index.

Inferring species richness and turnover by statistical multiresolution texture analysis of satellite imagery.

Directory of Open Access Journals (Sweden)

Matteo Convertino

Full Text Available BACKGROUND: The quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover. METHODOLOGY/PRINCIPAL FINDINGS: We model satellite images of regions of interest as textures. We define a texture in an image as a spatial domain where the variations in pixel intensity across the image are both stochastic and multiscale. To compare two textures quantitatively, we first obtain a multiresolution wavelet decomposition of each. Either an appropriate probability density function (pdf model for the coefficients at each subband is selected, and its parameters estimated, or, a non-parametric approach using histograms is adopted. We choose the former, where the wavelet coefficients of the multiresolution decomposition at each subband are modeled as samples from the generalized Gaussian pdf. We then obtain the joint pdf for the coefficients for all subbands, assuming independence across subbands; an approximation that simplifies the computational burden significantly without sacrificing the ability to statistically distinguish textures. We measure the difference between two textures' representative pdf's via the Kullback-Leibler divergence (KL. Species turnover, or [Formula: see text] diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species

An MGF-based unified framework to determine the joint statistics of partial sums of ordered random variables

KAUST Repository

Nam, Sungsik

2010-11-01

Order statistics find applications in various areas of communications and signal processing. In this paper, we introduce an unified analytical framework to determine the joint statistics of partial sums of ordered random variables (RVs). With the proposed approach, we can systematically derive the joint statistics of any partial sums of ordered statistics, in terms of the moment generating function (MGF) and the probability density function (PDF). Our MGF-based approach applies not only when all the K ordered RVs are involved but also when only the Ks(Ks < K) best RVs are considered. In addition, we present the closed-form expressions for the exponential RV special case. These results apply to the performance analysis of various wireless communication systems over fading channels. © 2006 IEEE.

A Model of Statistics Performance Based on Achievement Goal Theory.

Science.gov (United States)

Bandalos, Deborah L.; Finney, Sara J.; Geske, Jenenne A.

2003-01-01

Tests a model of statistics performance based on achievement goal theory. Both learning and performance goals affected achievement indirectly through study strategies, self-efficacy, and test anxiety. Implications of these findings for teaching and learning statistics are discussed. (Contains 47 references, 3 tables, 3 figures, and 1 appendix.)…

Improved Test Planning and Analysis Through the Use of Advanced Statistical Methods

Science.gov (United States)

Green, Lawrence L.; Maxwell, Katherine A.; Glass, David E.; Vaughn, Wallace L.; Barger, Weston; Cook, Mylan

2016-01-01

The goal of this work is, through computational simulations, to provide statistically-based evidence to convince the testing community that a distributed testing approach is superior to a clustered testing approach for most situations. For clustered testing, numerous, repeated test points are acquired at a limited number of test conditions. For distributed testing, only one or a few test points are requested at many different conditions. The statistical techniques of Analysis of Variance (ANOVA), Design of Experiments (DOE) and Response Surface Methods (RSM) are applied to enable distributed test planning, data analysis and test augmentation. The D-Optimal class of DOE is used to plan an optimally efficient single- and multi-factor test. The resulting simulated test data are analyzed via ANOVA and a parametric model is constructed using RSM. Finally, ANOVA can be used to plan a second round of testing to augment the existing data set with new data points. The use of these techniques is demonstrated through several illustrative examples. To date, many thousands of comparisons have been performed and the results strongly support the conclusion that the distributed testing approach outperforms the clustered testing approach.

Measuring the Success of an Academic Development Programme: A Statistical Analysis

Science.gov (United States)

Smith, L. C.

2009-01-01

This study uses statistical analysis to estimate the impact of first-year academic development courses in microeconomics, statistics, accountancy, and information systems, offered by the University of Cape Town's Commerce Academic Development Programme, on students' graduation performance relative to that achieved by mainstream students. The data…

Study design and statistical analysis of data in human population studies with the micronucleus assay.

Science.gov (United States)

Ceppi, Marcello; Gallo, Fabio; Bonassi, Stefano

2011-01-01

The most common study design performed in population studies based on the micronucleus (MN) assay, is the cross-sectional study, which is largely performed to evaluate the DNA damaging effects of exposure to genotoxic agents in the workplace, in the environment, as well as from diet or lifestyle factors. Sample size is still a critical issue in the design of MN studies since most recent studies considering gene-environment interaction, often require a sample size of several hundred subjects, which is in many cases difficult to achieve. The control of confounding is another major threat to the validity of causal inference. The most popular confounders considered in population studies using MN are age, gender and smoking habit. Extensive attention is given to the assessment of effect modification, given the increasing inclusion of biomarkers of genetic susceptibility in the study design. Selected issues concerning the statistical treatment of data have been addressed in this mini-review, starting from data description, which is a critical step of statistical analysis, since it allows to detect possible errors in the dataset to be analysed and to check the validity of assumptions required for more complex analyses. Basic issues dealing with statistical analysis of biomarkers are extensively evaluated, including methods to explore the dose-response relationship among two continuous variables and inferential analysis. A critical approach to the use of parametric and non-parametric methods is presented, before addressing the issue of most suitable multivariate models to fit MN data. In the last decade, the quality of statistical analysis of MN data has certainly evolved, although even nowadays only a small number of studies apply the Poisson model, which is the most suitable method for the analysis of MN data.

Statistical analysis of the potassium concentration obtained through

International Nuclear Information System (INIS)

Pereira, Joao Eduardo da Silva; Silva, Jose Luiz Silverio da; Pires, Carlos Alberto da Fonseca; Strieder, Adelir Jose

2007-01-01

The present work was developed in outcrops of Santa Maria region, southern Brazil, Rio Grande do Sul State. Statistic evaluations were applied in different rock types. The possibility to distinguish different geologic units, sedimentary and volcanic (acid and basic types) by means of the statistic analyses from the use of airborne gamma-ray spectrometry integrating potash radiation emissions data with geological and geochemistry data is discussed. This Project was carried out at 1973 by Geological Survey of Brazil/Companhia de Pesquisas de Recursos Minerais. The Camaqua Project evaluated the behavior of potash concentrations generating XYZ Geosof 1997 format, one grid, thematic map and digital thematic map files from this total area. Using these data base, the integration of statistics analyses in sedimentary formations which belong to the Depressao Central do Rio Grande do Sul and/or to volcanic rocks from Planalto da Serra Geral at the border of Parana Basin was tested. Univariate statistics model was used: the media, the standard media error, and the trust limits were estimated. The Tukey's Test was used in order to compare mean values. The results allowed to create criteria to distinguish geological formations based on their potash content. The back-calibration technique was employed to transform K radiation to percentage. Inside this context it was possible to define characteristic values from radioactive potash emissions and their trust ranges in relation to geologic formations. The potash variable when evaluated in relation to geographic Universal Transverse Mercator coordinates system showed a spatial relation following one polynomial model of second order, with one determination coefficient. The statistica 7.1 software Generalist Linear Models produced by Statistics Department of Federal University of Santa Maria/Brazil was used. (author)

Analysis of Variance in Statistical Image Processing

Science.gov (United States)

Kurz, Ludwik; Hafed Benteftifa, M.

1997-04-01

A key problem in practical image processing is the detection of specific features in a noisy image. Analysis of variance (ANOVA) techniques can be very effective in such situations, and this book gives a detailed account of the use of ANOVA in statistical image processing. The book begins by describing the statistical representation of images in the various ANOVA models. The authors present a number of computationally efficient algorithms and techniques to deal with such problems as line, edge, and object detection, as well as image restoration and enhancement. By describing the basic principles of these techniques, and showing their use in specific situations, the book will facilitate the design of new algorithms for particular applications. It will be of great interest to graduate students and engineers in the field of image processing and pattern recognition.

Study of relationship between MUF correlation and detection sensitivity of statistical analysis

International Nuclear Information System (INIS)

Tamura, Toshiaki; Ihara, Hitoshi; Yamamoto, Yoichi; Ikawa, Koji

1989-11-01

Various kinds of statistical analysis are proposed to NRTA (Near Real Time Materials Accountancy) which was devised to satisfy the timeliness goal of one of the detection goals of IAEA. It will be presumed that different statistical analysis results will occur between the case of considered rigorous error propagation (with MUF correlation) and the case of simplified error propagation (without MUF correlation). Therefore, measurement simulation and decision analysis were done using flow simulation of 800 MTHM/Y model reprocessing plant, and relationship between MUF correlation and detection sensitivity and false alarm of statistical analysis was studied. Specific character of material accountancy for 800 MTHM/Y model reprocessing plant was grasped by this simulation. It also became clear that MUF correlation decreases not only false alarm but also detection probability for protracted loss in case of CUMUF test and Page's test applied to NRTA. (author)