Global Testing And Large Scale Multiple Testing For High Dimensional Covariance Structures

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Multivariate Statistical Modeling in Engineering and Management

Author : Jhareswar Maiti
Publisher : CRC Press
ISBN 13 : 1000618390
Total Pages : 637 pages
Book Rating : 4.0/5 (6 download)

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Book Synopsis Multivariate Statistical Modeling in Engineering and Management by : Jhareswar Maiti

Download or read book Multivariate Statistical Modeling in Engineering and Management written by Jhareswar Maiti and published by CRC Press. This book was released on 2022-10-25 with total page 637 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on problem solving for practitioners and model building for academicians under multivariate situations. This book helps readers in understanding the issues, such as knowing variability, extracting patterns, building relationships, and making objective decisions. A large number of multivariate statistical models are covered in the book. The readers will learn how a practical problem can be converted to a statistical problem and how the statistical solution can be interpreted as a practical solution. Key features: Links data generation process with statistical distributions in multivariate domain Provides step by step procedure for estimating parameters of developed models Provides blueprint for data driven decision making Includes practical examples and case studies relevant for intended audiences The book will help everyone involved in data driven problem solving, modeling and decision making.

High-Dimensional Covariance Estimation

Author : Mohsen Pourahmadi
Publisher : John Wiley & Sons
ISBN 13 : 1118034295
Total Pages : 204 pages
Book Rating : 4.1/5 (18 download)

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Book Synopsis High-Dimensional Covariance Estimation by : Mohsen Pourahmadi

Download or read book High-Dimensional Covariance Estimation written by Mohsen Pourahmadi and published by John Wiley & Sons. This book was released on 2013-06-24 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and Regularization Regularizing the Eigenstructure Banding, Tapering, and Thresholding Covariance Matrices Sparse Gaussian Graphical Models Multivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.

Computational Statistics in Data Science

Author : Richard A. Levine
Publisher : John Wiley & Sons
ISBN 13 : 1119561086
Total Pages : 672 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Computational Statistics in Data Science by : Richard A. Levine

Download or read book Computational Statistics in Data Science written by Richard A. Levine and published by John Wiley & Sons. This book was released on 2022-03-23 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ein unverzichtbarer Leitfaden bei der Anwendung computergestützter Statistik in der modernen Datenwissenschaft In Computational Statistics in Data Science präsentiert ein Team aus bekannten Mathematikern und Statistikern eine fundierte Zusammenstellung von Konzepten, Theorien, Techniken und Praktiken der computergestützten Statistik für ein Publikum, das auf der Suche nach einem einzigen, umfassenden Referenzwerk für Statistik in der modernen Datenwissenschaft ist. Das Buch enthält etliche Kapitel zu den wesentlichen konkreten Bereichen der computergestützten Statistik, in denen modernste Techniken zeitgemäß und verständlich dargestellt werden. Darüber hinaus bietet Computational Statistics in Data Science einen kostenlosen Zugang zu den fertigen Einträgen im Online-Nachschlagewerk Wiley StatsRef: Statistics Reference Online. Außerdem erhalten die Leserinnen und Leser: * Eine gründliche Einführung in die computergestützte Statistik mit relevanten und verständlichen Informationen für Anwender und Forscher in verschiedenen datenintensiven Bereichen * Umfassende Erläuterungen zu aktuellen Themen in der Statistik, darunter Big Data, Datenstromverarbeitung, quantitative Visualisierung und Deep Learning Das Werk eignet sich perfekt für Forscher und Wissenschaftler sämtlicher Fachbereiche, die Techniken der computergestützten Statistik auf einem gehobenen oder fortgeschrittenen Niveau anwenden müssen. Zudem gehört Computational Statistics in Data Science in das Bücherregal von Wissenschaftlern, die sich mit der Erforschung und Entwicklung von Techniken der computergestützten Statistik und statistischen Grafiken beschäftigen.

Large Sample Covariance Matrices and High-Dimensional Data Analysis

Author : Jianfeng Yao
Publisher : Cambridge University Press
ISBN 13 : 9781107065178
Total Pages : 0 pages
Book Rating : 4.0/5 (651 download)

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Book Synopsis Large Sample Covariance Matrices and High-Dimensional Data Analysis by : Jianfeng Yao

Download or read book Large Sample Covariance Matrices and High-Dimensional Data Analysis written by Jianfeng Yao and published by Cambridge University Press. This book was released on 2015-03-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-dimensional data appear in many fields, and their analysis has become increasingly important in modern statistics. However, it has long been observed that several well-known methods in multivariate analysis become inefficient, or even misleading, when the data dimension p is larger than, say, several tens. A seminal example is the well-known inefficiency of Hotelling's T2-test in such cases. This example shows that classical large sample limits may no longer hold for high-dimensional data; statisticians must seek new limiting theorems in these instances. Thus, the theory of random matrices (RMT) serves as a much-needed and welcome alternative framework. Based on the authors' own research, this book provides a first-hand introduction to new high-dimensional statistical methods derived from RMT. The book begins with a detailed introduction to useful tools from RMT, and then presents a series of high-dimensional problems with solutions provided by RMT methods.

Handbook of International Large-Scale Assessment

Author : Leslie Rutkowski
Publisher : CRC Press
ISBN 13 : 1439895147
Total Pages : 623 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Handbook of International Large-Scale Assessment by : Leslie Rutkowski

Download or read book Handbook of International Large-Scale Assessment written by Leslie Rutkowski and published by CRC Press. This book was released on 2013-11-12 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner of the 2017 AERA Division D Significant Contribution to Educational Measurement and Research Methodology Award! Technological and statistical advances, along with a strong interest in gathering more information about the state of our educational systems, have made it possible to assess more students, in more countries, more often, and in more subject domains. The Handbook of International Large-Scale Assessment: Background, Technical Issues, and Methods of Data Analysis brings together recognized scholars in the field of ILSA, behavioral statistics, and policy to develop a detailed guide that goes beyond database user manuals. After highlighting the importance of ILSA data to policy and research, the book reviews methodological aspects and features of the studies based on operational considerations, analytics, and reporting. The book then describes methods of interest to advanced graduate students, researchers, and policy analysts who have a good grounding in quantitative methods, but who are not necessarily quantitative methodologists. In addition, it provides a detailed exposition of the technical details behind these assessments, including the test design, the sampling framework, and estimation methods, with a focus on how these issues impact analysis choices.

Issues in Statistics, Decision Making, and Stochastics: 2013 Edition

Author :
Publisher : ScholarlyEditions
ISBN 13 : 1490110828
Total Pages : 314 pages
Book Rating : 4.4/5 (91 download)

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Book Synopsis Issues in Statistics, Decision Making, and Stochastics: 2013 Edition by :

Download or read book Issues in Statistics, Decision Making, and Stochastics: 2013 Edition written by and published by ScholarlyEditions. This book was released on 2013-05-01 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues in Statistics, Decision Making, and Stochastics: 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Regular and Chaotic Dynamics. The editors have built Issues in Statistics, Decision Making, and Stochastics: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Regular and Chaotic Dynamics in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Statistics, Decision Making, and Stochastics: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Introduction to Nonparametric Estimation

Author : Alexandre B. Tsybakov
Publisher : Springer Science & Business Media
ISBN 13 : 0387790527
Total Pages : 222 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Introduction to Nonparametric Estimation by : Alexandre B. Tsybakov

Download or read book Introduction to Nonparametric Estimation written by Alexandre B. Tsybakov and published by Springer Science & Business Media. This book was released on 2008-10-22 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.

Statistical Analysis for High-Dimensional Data

Author : Arnoldo Frigessi
Publisher : Springer
ISBN 13 : 3319270990
Total Pages : 313 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Statistical Analysis for High-Dimensional Data by : Arnoldo Frigessi

Download or read book Statistical Analysis for High-Dimensional Data written by Arnoldo Frigessi and published by Springer. This book was released on 2016-02-16 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.

New Perspectives and Challenges in Econophysics and Sociophysics

Author : Frédéric Abergel
Publisher : Springer
ISBN 13 : 3030113647
Total Pages : 269 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis New Perspectives and Challenges in Econophysics and Sociophysics by : Frédéric Abergel

Download or read book New Perspectives and Challenges in Econophysics and Sociophysics written by Frédéric Abergel and published by Springer. This book was released on 2019-04-02 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest perspectives and challenges within the interrelated fields of econophysics and sociophysics, which have emerged from the application of statistical physics to economics and sociology. Economic and financial markets appear to be in a permanent state of flux. Billions of agents interact with each other, giving rise to complex dynamics of economic quantities at the micro and macro levels. With the availability of huge data sets, researchers can address questions at a much more granular level than was previously possible. Fundamental questions regarding the aggregation of actions and information and the coordination, complexity, and evolution of economic and financial networks are currently receiving much attention in the econophysics research agenda. In parallel, the sociophysics literature has focused on large-scale social data and their interrelations. In this book, leading researchers from different communities – economists, sociologists, financial analysts, mathematicians, physicists, statisticians, and others – report on their recent work and their analyses of economic and social behavior.

High-Dimensional Probability

Author : Roman Vershynin
Publisher : Cambridge University Press
ISBN 13 : 1108415199
Total Pages : 299 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis High-Dimensional Probability by : Roman Vershynin

Download or read book High-Dimensional Probability written by Roman Vershynin and published by Cambridge University Press. This book was released on 2018-09-27 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

Resampling-Based Multiple Testing

Author : Peter H. Westfall
Publisher : John Wiley & Sons
ISBN 13 : 9780471557616
Total Pages : 382 pages
Book Rating : 4.5/5 (576 download)

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Book Synopsis Resampling-Based Multiple Testing by : Peter H. Westfall

Download or read book Resampling-Based Multiple Testing written by Peter H. Westfall and published by John Wiley & Sons. This book was released on 1993-01-12 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combines recent developments in resampling technology (including the bootstrap) with new methods for multiple testing that are easy to use, convenient to report and widely applicable. Software from SAS Institute is available to execute many of the methods and programming is straightforward for other applications. Explains how to summarize results using adjusted p-values which do not necessitate cumbersome table look-ups. Demonstrates how to incorporate logical constraints among hypotheses, further improving power.

Statistical Foundations of Data Science

Author : Jianqing Fan
Publisher : CRC Press
ISBN 13 : 0429527616
Total Pages : 974 pages
Book Rating : 4.4/5 (295 download)

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Book Synopsis Statistical Foundations of Data Science by : Jianqing Fan

Download or read book Statistical Foundations of Data Science written by Jianqing Fan and published by CRC Press. This book was released on 2020-09-21 with total page 974 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

Symmetric Multivariate and Related Distributions

Author : Kai Wang Fang
Publisher : CRC Press
ISBN 13 : 1351093940
Total Pages : 165 pages
Book Rating : 4.3/5 (51 download)

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Book Synopsis Symmetric Multivariate and Related Distributions by : Kai Wang Fang

Download or read book Symmetric Multivariate and Related Distributions written by Kai Wang Fang and published by CRC Press. This book was released on 2018-01-18 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the publication of the by now classical Johnson and Kotz Continuous Multivariate Distributions (Wiley, 1972) there have been substantial developments in multivariate distribution theory especially in the area of non-normal symmetric multivariate distributions. The book by Fang, Kotz and Ng summarizes these developments in a manner which is accessible to a reader with only limited background (advanced real-analysis calculus, linear algebra and elementary matrix calculus). Many of the results in this field are due to Kai-Tai Fang and his associates and appeared in Chinese publications only. A thorough literature search was conducted and the book represents the latest work - as of 1988 - in this rapidly developing field of multivariate distributions. The authors are experts in statistical distribution theory.

High-dimensional Data Analysis

Author : Tony Cai;Xiaotong Shen
Publisher :
ISBN 13 : 9787894236326
Total Pages : 318 pages
Book Rating : 4.2/5 (363 download)

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Book Synopsis High-dimensional Data Analysis by : Tony Cai;Xiaotong Shen

Download or read book High-dimensional Data Analysis written by Tony Cai;Xiaotong Shen and published by . This book was released on with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last few years, significant developments have been taking place in highdimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics and signal processing. In particular, substantial advances have been made in the areas of feature selection, covariance estimation, classification and regression. This book intends to examine important issues arising from highdimensional data analysis to explore key ideas for statistical inference and prediction. It is structured around topics on multiple hypothesis testing, feature selection, regression, cla.

Statistical Modelling and Regression Structures

Author : Thomas Kneib
Publisher : Springer Science & Business Media
ISBN 13 : 3790824135
Total Pages : 486 pages
Book Rating : 4.7/5 (98 download)

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Book Synopsis Statistical Modelling and Regression Structures by : Thomas Kneib

Download or read book Statistical Modelling and Regression Structures written by Thomas Kneib and published by Springer Science & Business Media. This book was released on 2010-01-12 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributions collected in this book have been written by well-known statisticians to acknowledge Ludwig Fahrmeir's far-reaching impact on Statistics as a science, while celebrating his 65th birthday. The contributions cover broad areas of contemporary statistical model building, including semiparametric and geoadditive regression, Bayesian inference in complex regression models, time series modelling, statistical regularization, graphical models and stochastic volatility models.

Statistics for High-Dimensional Data

Author : Peter Bühlmann
Publisher : Springer Science & Business Media
ISBN 13 : 364220192X
Total Pages : 568 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Statistics for High-Dimensional Data by : Peter Bühlmann

Download or read book Statistics for High-Dimensional Data written by Peter Bühlmann and published by Springer Science & Business Media. This book was released on 2011-06-08 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.

Models for Discrete Data

Author : Daniel Zelterman
Publisher : Oxford University Press
ISBN 13 : 9780198524366
Total Pages : 233 pages
Book Rating : 4.5/5 (243 download)

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Book Synopsis Models for Discrete Data by : Daniel Zelterman

Download or read book Models for Discrete Data written by Daniel Zelterman and published by Oxford University Press. This book was released on 1999 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discrete or count data arise in experiments where the outcome variables are the numbers of individuals classified into unique, non-overlapping categories. This book describes the statistical models used in the analysis and summary of such data, and provides an introduction to the subject for graduate students and practitioners needing a review of the methodology. It includes topics not covered in depth elsewhere, such as the negative multinomial distribution; the many forms of the hypergeometric distribution; and coordinate free models.