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/Title (Access Free Statistics For High Dimensional Data Methods Theory And Applications ? - zurcoin.org)
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BT 34.016 338.580 Td /F1 28.5 Tf [(Statistics For High Dimensional Data )] TJ ET
BT 34.016 303.782 Td /F1 28.5 Tf [(Methods Theory And Applications)] TJ ET
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BT 34.016 357.345 Td /F1 14.2 Tf [(Getting the books )] TJ ET
BT 149.669 357.345 Td /F1 14.2 Tf [(Statistics For High Dimensional Data Methods Theory And)] TJ ET
BT 34.016 339.946 Td /F1 14.2 Tf [(Applications)] TJ ET
BT 110.837 339.946 Td /F1 14.2 Tf [( now is not type of inspiring means. You could not and no-one else )] TJ ET
BT 34.016 322.546 Td /F1 14.2 Tf [(going similar to books accrual or library or borrowing from your links to gain access )] TJ ET
BT 34.016 305.147 Td /F1 14.2 Tf [(to them. This is an totally easy means to specifically get lead by on-line. This )] TJ ET
BT 34.016 287.748 Td /F1 14.2 Tf [(online notice Statistics For High Dimensional Data Methods Theory And )] TJ ET
BT 34.016 270.349 Td /F1 14.2 Tf [(Applications can be one of the options to accompany you afterward having other )] TJ ET
BT 34.016 252.949 Td /F1 14.2 Tf [(time.)] TJ ET
BT 34.016 218.450 Td /F1 14.2 Tf [(It will not waste your time. give a positive response me, the e-book will totally way )] TJ ET
BT 34.016 201.051 Td /F1 14.2 Tf [(of being you additional thing to read. Just invest tiny become old to gain access to )] TJ ET
BT 34.016 183.652 Td /F1 14.2 Tf [(this on-line publication )] TJ ET
BT 178.952 183.652 Td /F1 14.2 Tf [(Statistics For High Dimensional Data Methods Theory And)] TJ ET
BT 34.016 166.252 Td /F1 14.2 Tf [(Applications)] TJ ET
BT 110.837 166.252 Td /F1 14.2 Tf [( as capably as review them wherever you are now.)] TJ ET
BT 34.016 103.253 Td /F1 14.2 Tf [(High-Dimensional Data Analysis with Low-Dimensional Models)] TJ ET
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BT 429.938 103.253 Td /F1 14.2 Tf [( John Wright 2021-)] TJ ET
BT 34.016 85.854 Td /F1 14.2 Tf [(12-31 Connects fundamental mathematical theory with real-world problems, )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(through efficient and scalable optimization algorithms.)] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(Contributions in infinite-dimensional statistics and related topics)] TJ ET
BT 433.956 354.196 Td /F1 14.2 Tf [( Enea G. )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(Bongiorno 2014-05-21 The interest towards Functional and Operatorial Statistics, )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(and, more in general, towards infinite-dimensional statistics has dramatically )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(increased in the statistical community and in many other applied scientific areas )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(where people faces functional data. This volume collects the works selected and )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(presented at the Third Edition of the International Workshop on Functional and )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(Operatorial Statistics held in Stresa, Italy, from the 19th to the 21st of June 2014 )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(\(IWFOS’2014\). The meeting represents an opportunity of bringing together leading )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(researchers active on these topics both for what concerns theoretical aspects and )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(a wide range of applications in various fields. To promote collaborations with other )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(important strictly related areas of infinite-dimensional Statistics, such as High )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(Dimensional Statistics and Model Selection Procedures, this book hosts works in )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(the latter research subjects too.)] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(Modern Directional Statistics)] TJ ET
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34.016 125.654 m 214.563 125.654 l S
BT 214.563 128.005 Td /F1 14.2 Tf [( Christophe Ley 2017-08-03 Modern Directional )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(Statistics collects important advances in methodology and theory for directional )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(statistics over the last two decades. It provides a detailed overview and analysis of )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(recent results that can help both researchers and practitioners. Knowledge of )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(multivariate statistics eases the reading but is not mandatory. The field of )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(directional statistics has received a lot of attention over the past two decades, due )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(to new demands from domains such as life sciences or machine learning, to the )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(availability of massive data sets requiring adapted statistical techniques, and to )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(technological advances. This book covers important progresses in distribution )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(theory,high-dimensional statistics, kernel density estimation, efficient inference on )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(directional supports, and computational and graphical methods. Christophe Ley is )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(professor of mathematical statistics at Ghent University. His research interests )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(include semi-parametrically efficient inference, flexible modeling, directional )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(statistics and the study of asymptotic approximations via Stein’s Method. His )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(achievements include the Marie-Jeanne Laurent-Duhamel prize of the Société )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(Française de Statistique and an elected membership at the International Statistical )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(Institute. He is associate editor for the journals Computational Statistics & Data )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(Analysis and Econometrics and Statistics. Thomas Verdebout is professor of )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(mathematical statistics at Université libre de Bruxelles \(ULB\). His main research )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(interests are semi-parametric statistics, high- dimensional statistics, directional )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(statistics and rank-based procedures. He has won an annual prize of the Belgian )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(Academy of Sciences and is an elected member of the International Statistical )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(Institute. He is associate editor for the journals Statistics and Probability Letters )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(and Journal of Multivariate Analysis.)] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(High-Dimensional Statistics)] TJ ET
BT 207.424 354.196 Td /F1 14.2 Tf [( Martin J. Wainwright 2019-02-21 A coherent )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(introductory text from a groundbreaking researcher, focusing on clarity and )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(motivation to build intuition and understanding.)] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(High-Dimensional Probability)] TJ ET
BT 216.929 301.998 Td /F1 14.2 Tf [( Roman Vershynin 2018-09-27 An integrated )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(package of powerful probabilistic tools and key applications in modern )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(mathematical data science.)] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(New Frontiers of Biostatistics and Bioinformatics)] TJ ET
BT 338.894 249.800 Td /F1 14.2 Tf [( Yichuan Zhao 2018-12-05 This )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(book is comprised of presentations delivered at the 5th Workshop on Biostatistics )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(and Bioinformatics held in Atlanta on May 5-7, 2017. Featuring twenty-two )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(selected papers from the workshop, this book showcases the most current )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(advances in the field, presenting new methods, theories, and case applications at )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(the frontiers of biostatistics, bioinformatics, and interdisciplinary areas. Biostatistics )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(and bioinformatics have been playing a key role in statistics and other scientific )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(research fields in recent years. The goal of the 5th Workshop on Biostatistics and )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(Bioinformatics was to stimulate research, foster interaction among researchers in )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(field, and offer opportunities for learning and facilitating research collaborations in )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(the era of big data. The resulting volume offers timely insights for researchers, )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(students, and industry practitioners.)] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(Theoretical Statistics)] TJ ET
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34.016 351.844 m 165.472 351.844 l S
BT 165.472 354.196 Td /F1 14.2 Tf [( Robert W. Keener 2010-09-08 Intended as the text for a )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(sequence of advanced courses, this book covers major topics in theoretical )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(statistics in a concise and rigorous fashion. The discussion assumes a background )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(in advanced calculus, linear algebra, probability, and some analysis and topology. )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(Measure theory is used, but the notation and basic results needed are presented )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(in an initial chapter on probability, so prior knowledge of these topics is not )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(essential. The presentation is designed to expose students to as many of the )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(central ideas and topics in the discipline as possible, balancing various )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(approaches to inference as well as exact, numerical, and large sample methods. )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(Moving beyond more standard material, the book includes chapters introducing )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(bootstrap methods, nonparametric regression, equivariant estimation, empirical )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(Bayes, and sequential design and analysis. The book has a rich collection of )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(exercises. Several of them illustrate how the theory developed in the book may be )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(used in various applications. Solutions to many of the exercises are included in an )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(appendix.)] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(Geometric Structure of High-Dimensional Data and Dimensionality Reduction)] TJ ET
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BT 34.016 75.808 Td /F1 14.2 Tf [(Jianzhong Wang 2012-04-28 "Geometric Structure of High-Dimensional Data and )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(Dimensionality Reduction" adopts data geometry as a framework to address )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(various methods of dimensionality reduction. In addition to the introduction to well-)] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(known linear methods, the book moreover stresses the recently developed )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(nonlinear methods and introduces the applications of dimensionality reduction in )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(many areas, such as face recognition, image segmentation, data classification, )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(data visualization, and hyperspectral imagery data analysis. Numerous tables and )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(graphs are included to illustrate the ideas, effects, and shortcomings of the )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(methods. MATLAB code of all dimensionality reduction algorithms is provided to )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(aid the readers with the implementations on computers. The book will be useful for )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(mathematicians, statisticians, computer scientists, and data analysts. It is also a )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(valuable handbook for other practitioners who have a basic background in )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(mathematics, statistics and/or computer algorithms, like internet search engine )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(designers, physicists, geologists, electronic engineers, and economists. Jianzhong )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(Wang is a Professor of Mathematics at Sam Houston State University, U.S.A.)] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(Principles and Theory for Data Mining and Machine Learning)] TJ ET
BT 417.341 145.405 Td /F1 14.2 Tf [( Bertrand Clarke )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(2009-07-21 Extensive treatment of the most up-to-date topics Provides the theory )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(and concepts behind popular and emerging methods Range of topics drawn from )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(Statistics, Computer Science, and Electrical Engineering)] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(Big and Complex Data Analysis)] TJ ET
BT 233.587 75.808 Td /F1 14.2 Tf [( S. Ejaz Ahmed 2017-03-21 This volume conveys )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(some of the surprises, puzzles and success stories in high-dimensional and )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(complex data analysis and related fields. Its peer-reviewed contributions showcase )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(recent advances in variable selection, estimation and prediction strategies for a )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(host of useful models, as well as essential new developments in the field. The )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(continued and rapid advancement of modern technology now allows scientists to )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(collect data of increasingly unprecedented size and complexity. Examples include )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(epigenomic data, genomic data, proteomic data, high-resolution image data, high-)] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(frequency financial data, functional and longitudinal data, and network data. )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(Simultaneous variable selection and estimation is one of the key statistical )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(problems involved in analyzing such big and complex data. The purpose of this )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(book is to stimulate research and foster interaction between researchers in the )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(area of high-dimensional data analysis. More concretely, its goals are to: 1\) )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(highlight and expand the breadth of existing methods in big data and high-)] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(dimensional data analysis and their potential for the advancement of both the )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(mathematical and statistical sciences; 2\) identify important directions for future )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(research in the theory of regularization methods, in algorithmic development, and )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(in methodologies for different application areas; and 3\) facilitate collaboration )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(between theoretical and subject-specific researchers.)] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(Introduction to High-Dimensional Statistics)] TJ ET
BT 301.688 75.808 Td /F1 14.2 Tf [( Christophe Giraud 2014-12-17 Ever-)] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(greater computing technologies have given rise to an exponentially growing )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(volume of data. Today massive data sets \(with potentially thousands of variables\) )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(play an important role in almost every branch of modern human activity, including )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(networks, finance, and genetics. However, analyzing such data has presented a )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(challenge for statisticians and data analysts and has required the development of )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(new statistical methods capable of separating the signal from the noise. )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(models, techniques, and approaches for handling high-dimensional data. The book )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(is intended to expose the reader to the key concepts and ideas in the most simple )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(settings possible while avoiding unnecessary technicalities. Offering a succinct )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(presentation of the mathematical foundations of high-dimensional statistics, this )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(highly accessible text: Describes the challenges related to the analysis of high-)] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(dimensional data Covers cutting-edge statistical methods including model )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(selection, sparsity and the lasso, aggregation, and learning theory Provides )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(detailed exercises at the end of every chapter with collaborative solutions on a )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(wikisite Illustrates concepts with simple but clear practical examples Introduction to )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(High-Dimensional Statistics is suitable for graduate students and researchers )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(interested in discovering modern statistics for massive data. It can be used as a )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(graduate text or for self-study.)] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(Statistical Learning with Sparsity)] TJ ET
BT 239.130 58.408 Td /F1 14.2 Tf [( Trevor Hastie 2015-05-07 Discover New Methods )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(for Dealing with High-Dimensional Data A sparse statistical model has only a small )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(number of nonzero parameters or weights; therefore, it is much easier to estimate )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(and interpret than a dense model. Statistical Learning with Sparsity: The Lasso )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(and Generalizations presents methods that exploit sparsity to help recover the )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(underlying signal in a set of data. Top experts in this rapidly evolving field, the )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(authors describe the lasso for linear regression and a simple coordinate descent )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(algorithm for its computation. They discuss the application of l1 penalties to )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(generalized linear models and support vector machines, cover generalized )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(penalties such as the elastic net and group lasso, and review numerical methods )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(for optimization. They also present statistical inference methods for fitted \(lasso\) )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(models, including the bootstrap, Bayesian methods, and recently developed )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(approaches. In addition, the book examines matrix decomposition, sparse )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(multivariate analysis, graphical models, and compressed sensing. It concludes )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(with a survey of theoretical results for the lasso. In this age of big data, the number )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(of features measured on a person or object can be large and might be larger than )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(the number of observations. This book shows how the sparsity assumption allows )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(us to tackle these problems and extract useful and reproducible patterns from big )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(datasets. Data analysts, computer scientists, and theorists will appreciate this )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(thorough and up-to-date treatment of sparse statistical modeling.)] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(Introduction to High-Dimensional Statistics)] TJ ET
BT 301.688 354.196 Td /F1 14.2 Tf [( Christophe Giraud 2014-12-17 Ever-)] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(greater computing technologies have given rise to an exponentially growing )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(volume of data. Today massive data sets \(with potentially thousands of variables\) )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(play an important role in almost every branch of modern human activity, including )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(networks, finance, and genetics. However, analyzing such data has presented a )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(challenge for statisticians and data analysts and has required the development of )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(new statistical methods capable of separating the signal from the noise. )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(models, techniques, and approaches for handling high-dimensional data. The book )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(is intended to expose the reader to the key concepts and ideas in the most simple )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(settings possible while avoiding unnecessary technicalities. Offering a succinct )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(presentation of the mathematical foundations of high-dimensional statistics, this )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(highly accessible text: Describes the challenges related to the analysis of high-)] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(dimensional data Covers cutting-edge statistical methods including model )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(selection, sparsity and the lasso, aggregation, and learning theory Provides )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(detailed exercises at the end of every chapter with collaborative solutions on a )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(wikisite Illustrates concepts with simple but clear practical examples Introduction to )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(High-Dimensional Statistics is suitable for graduate students and researchers )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(interested in discovering modern statistics for massive data. It can be used as a )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(graduate text or for self-study.)] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(Modern Multivariate Statistical Techniques)] TJ ET
BT 301.688 336.796 Td /F1 14.2 Tf [( Alan J. Izenman 2009-03-02 This is the )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(first book on multivariate analysis to look at large data sets which describes the )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(state of the art in analyzing such data. Material such as database management )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(systems is included that has never appeared in statistics books before.)] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(Statistical Analysis for High-Dimensional Data)] TJ ET
BT 323.048 267.199 Td /F1 14.2 Tf [( Arnoldo Frigessi 2016-02-16 This )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(book features research contributions from The Abel Symposium on Statistical )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(2014. The focus of the symposium was on statistical and machine learning )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(methodologies specifically developed for inference in “big data” situations, with )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(particular reference to genomic applications. The contributors, who are among the )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(most prominent researchers on the theory of statistics for high dimensional )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(inference, present new theories and methods, as well as challenging applications )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(and computational solutions. Specific themes include, among others, variable )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(selection and screening, penalised regression, sparsity, thresholding, low )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(dimensional structures, computational challenges, non-convex situations, learning )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(graphical models, sparse covariance and precision matrices, semi- and non-)] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(parametric formulations, multiple testing, classification, factor models, clustering, )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(and preselection. Highlighting cutting-edge research and casting light on future )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(research directions, the contributions will benefit graduate students and )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(researchers in computational biology, statistics and the machine learning )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(community.)] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(Functional and High-Dimensional Statistics and Related Fields)] TJ ET
0.285 w 0 J [ ] 0 d
34.016 299.647 m 427.615 299.647 l S
BT 427.615 301.998 Td /F1 14.2 Tf [( Germán Aneiros )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(2020-06-19 This book presents the latest research on the statistical analysis of )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(functional, high-dimensional and other complex data, addressing methodological )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(and computational aspects, as well as real-world applications. It covers topics like )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(classification, confidence bands, density estimation, depth, diagnostic tests, )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(dimension reduction, estimation on manifolds, high- and infinite-dimensional )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(statistics, inference on functional data, networks, operatorial statistics, prediction, )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(regression, robustness, sequential learning, small-ball probability, smoothing, )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(spatial data, testing, and topological object data analysis, and includes applications )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(in automobile engineering, criminology, drawing recognition, economics, )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(environmetrics, medicine, mobile phone data, spectrometrics and urban )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(environments. The book gathers selected, refereed contributions presented at the )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(Fifth International Workshop on Functional and Operatorial Statistics \(IWFOS\) in )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(Brno, Czech Republic. The workshop was originally to be held on June 24-26, )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(2020, but had to be postponed as a consequence of the COVID-19 pandemic. )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(Initiated by the Working Group on Functional and Operatorial Statistics at the )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(University of Toulouse in 2008, the IWFOS workshops provide a forum to discuss )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(the latest trends and advances in functional statistics and related fields, and foster )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(the exchange of ideas and international collaboration in the field.)] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(High-dimensional Data Analysis)] TJ ET
BT 235.154 301.998 Td /F1 14.2 Tf [( Tianwen Tony Cai 2011 Over the last few years, )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(significant developments have been taking place in high-dimensional data )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(analysis, driven primarily by a wide range of applications in many fields such as )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(genomics and signal processing. In particular, substantial advances have been )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(made in the areas of feature selection, covariance estimation, classification and )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(regression. This book intends to examine important issues arising from high-)] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(dimensional data analysis to explore key ideas for statistical inference and )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(prediction. It is structured around topics on multiple hypothesis testing, feature )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(selection, regression, classification, dimension reduction, as well as applications in )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(survival analysis and biomedical research. The book will appeal to graduate )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(students and new researchers interested in the plethora of opportunities available )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(in high-dimensional data analysis.)] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(Statistics for High-Dimensional Data)] TJ ET
0.285 w 0 J [ ] 0 d
34.016 90.856 m 262.073 90.856 l S
BT 262.073 93.207 Td /F1 14.2 Tf [( Peter Bühlmann 2011-06-08 Modern statistics )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(deals with large and complex data sets, and consequently with models containing )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(a large number of parameters. This book presents a detailed account of recently )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(developed approaches, including the Lasso and versions of it for various models, )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(boosting methods, undirected graphical modeling, and procedures controlling false )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(positive selections. A special characteristic of the book is that it contains )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(comprehensive mathematical theory on high-dimensional statistics combined with )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(methodology, algorithms and illustrations with real data examples. This in-depth )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(approach highlights the methods’ great potential and practical applicability in a )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(variety of settings. As such, it is a valuable resource for researchers, graduate )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(students and experts in statistics, applied mathematics and computer science.)] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(Model-Based Clustering and Classification for Data Science)] TJ ET
BT 410.985 232.401 Td /F1 14.2 Tf [( Charles Bouveyron )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(2019-09-30 Cluster analysis finds groups in data automatically. Most methods )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(have been heuristic and leave open such central questions as: how many clusters )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(are there? Which method should I use? How should I handle outliers? )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(Classification assigns new observations to groups given previously classified )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(observations, and also has open questions about parameter tuning, robustness )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(and uncertainty assessment. This book frames cluster analysis and classification )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(in terms of statistical models, thus yielding principled estimation, testing and )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(prediction methods, and sound answers to the central questions. It builds the basic )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(ideas in an accessible but rigorous way, with extensive data examples and R code; )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(describes modern approaches to high-dimensional data and networks; and )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(explains such recent advances as Bayesian regularization, non-Gaussian model-)] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(based clustering, cluster merging, variable selection, semi-supervised and robust )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(classification, clustering of functional data, text and images, and co-clustering. )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(Written for advanced undergraduates in data science, as well as researchers and )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(probability and statistics.)] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(Inverse Problems and High-Dimensional Estimation)] TJ ET
BT 359.486 267.199 Td /F1 14.2 Tf [( Pierre Alquier 2011-06-07 The )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(“Stats in the Château” summer school was held at the CRC château on the )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(campus of HEC Paris, Jouy-en-Josas, France, from August 31 to September 4, )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(2009. This event was organized jointly by faculty members of three French )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(academic institutions ? ENSAE ParisTech, the Ecole Polytechnique ParisTech, )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(and HEC Paris ? which cooperate through a scientific foundation devoted to the )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(decision sciences. The scientific content of the summer school was conveyed in )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(two courses, one by Laurent Cavalier \(Université Aix-Marseille I\) on "Ill-posed )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(Inverse Problems", and one by Victor Chernozhukov \(Massachusetts Institute of )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(Technology\) on "High-dimensional Estimation with Applications to Economics". )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(Ten invited researchers also presented either reviews of the state of the art in the )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(field or of applications, or original research contributions. This volume contains the )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(lecture notes of the two courses. Original research articles and a survey )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(complement these lecture notes. Applications to economics are discussed in )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(various contributions.)] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(Multiple Testing Procedures with Applications to Genomics)] TJ ET
0.285 w 0 J [ ] 0 d
34.016 334.445 m 404.644 334.445 l S
BT 404.644 336.796 Td /F1 14.2 Tf [( Sandrine Dudoit 2007-)] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(12-18 This book establishes the theoretical foundations of a general methodology )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(for multiple hypothesis testing and discusses its software implementation in R and )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(SAS. These are applied to a range of problems in biomedical and genomic )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(research, including identification of differentially expressed and co-expressed )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(genes in high-throughput gene expression experiments; tests of association )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(between gene expression measures and biological annotation metadata; )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(sequence analysis; and genetic mapping of complex traits using single nucleotide )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(polymorphisms. The procedures are based on a test statistics joint null distribution )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(and provide Type I error control in testing problems involving general data )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(generating distributions, null hypotheses, and test statistics.)] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(Principles and Methods for Data Science)] TJ ET
0.285 w 0 J [ ] 0 d
34.016 143.053 m 291.413 143.053 l S
BT 291.413 145.405 Td /F1 14.2 Tf [( 2020-05-28 Principles and Methods for )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(Data Science, Volume 43 in the Handbook of Statistics series, highlights new )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(advances in the field, with this updated volume presenting interesting and timely )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(topics, including Competing risks, aims and methods, Data analysis and mining of )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(microbial community dynamics, Support Vector Machines, a robust prediction )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(method with applications in bioinformatics, Bayesian Model Selection for Data with )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(High Dimension, High dimensional statistical inference: theoretical development to )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(data analytics, Big data challenges in genomics, Analysis of microarray gene )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(expression data using information theory and stochastic algorithm, Hybrid Models, )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(Markov Chain Monte Carlo Methods: Theory and Practice, and more. Provides the )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(authority and expertise of leading contributors from an international board of )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(authors Presents the latest release in the Handbook of Statistics series Updated )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(release includes the latest information on Principles and Methods for Data Science)] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(Analysis of Multivariate and High-Dimensional Data)] TJ ET
0.285 w 0 J [ ] 0 d
34.016 247.449 m 357.904 247.449 l S
BT 357.904 249.800 Td /F1 14.2 Tf [( Inge Koch 2013-12-02 This )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(modern approach integrates classical and contemporary methods, fusing theory )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(and practice and bridging the gap to statistical learning.)] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(Introduction to High-Dimensional Statistics)] TJ ET
BT 301.688 197.602 Td /F1 14.2 Tf [( Christophe Giraud 2021-08-26 Praise )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(for the first edition: "[This book] succeeds singularly at providing a structured )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(introduction to this active field of research. ... it is arguably the most accessible )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(overview yet published of the mathematical ideas and principles that one needs to )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(master to enter the field of high-dimensional statistics. ... recommended to anyone )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(interested in the main results of current research in high-dimensional statistics as )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(well as anyone interested in acquiring the core mathematical skills to enter this )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(area of research." —Journal of the American Statistical Association Introduction to )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(High-Dimensional Statistics, Second Edition preserves the philosophy of the first )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(edition: to be a concise guide for students and researchers discovering the area )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(and interested in the mathematics involved. The main concepts and ideas are )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(presented in simple settings, avoiding thereby unessential technicalities. High-)] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(dimensional statistics is a fast-evolving field, and much progress has been made )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(on a large variety of topics, providing new insights and methods. Offering a )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(succinct presentation of the mathematical foundations of high-dimensional )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(statistics, this new edition: Offers revised chapters from the previous edition, with )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(the inclusion of many additional materials on some important topics, including )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(compress sensing, estimation with convex constraints, the slope estimator, )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(simultaneously low-rank and row-sparse linear regression, or aggregation of a )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(continuous set of estimators. Introduces three new chapters on iterative )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(algorithms, clustering, and minimax lower bounds. Provides enhanced appendices, )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(minimax lower-bounds mainly with the addition of the Davis-Kahan perturbation )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(bound and of two simple versions of the Hanson-Wright concentration inequality. )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(Covers cutting-edge statistical methods including model selection, sparsity and the )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(Lasso, iterative hard thresholding, aggregation, support vector machines, and )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(learning theory. Provides detailed exercises at the end of every chapter with )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(collaborative solutions on a wiki site. Illustrates concepts with simple but clear )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(practical examples.)] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(Spectral Analysis of Large Dimensional Random Matrices)] TJ ET
BT 397.505 354.196 Td /F1 14.2 Tf [( Zhidong Bai 2009-12-10 )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(The aim of the book is to introduce basic concepts, main results, and widely )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(applied mathematical tools in the spectral analysis of large dimensional random )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(matrices. The core of the book focuses on results established under moment )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(conditions on random variables using probabilistic methods, and is thus easily )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(applicable to statistics and other areas of science. The book introduces )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(fundamental results, most of them investigated by the authors, such as the )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(semicircular law of Wigner matrices, the Marcenko-Pastur law, the limiting spectral )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(distribution of the multivariate F matrix, limits of extreme eigenvalues, spectrum )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(separation theorems, convergence rates of empirical distributions, central limit )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(theorems of linear spectral statistics, and the partial solution of the famous circular )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(law. While deriving the main results, the book simultaneously emphasizes the )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(ideas and methodologies of the fundamental mathematical tools, among them )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(being: truncation techniques, matrix identities, moment convergence theorems, )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(and the Stieltjes transform. Its treatment is especially fitting to the needs of )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(mathematics and statistics graduate students and beginning researchers, having a )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(basic knowledge of matrix theory and an understanding of probability theory at the )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(graduate level, who desire to learn the concepts and tools in solving problems in )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(this area. It can also serve as a detailed handbook on results of large dimensional )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(random matrices for practical users. This second edition includes two additional )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(chapters, one on the authors' results on the limiting behavior of eigenvectors of )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(sample covariance matrices, another on applications to wireless communications )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(and finance. While attempting to bring this edition up-to-date on recent work, it also )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(provides summaries of other areas which are typically considered part of the )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(general field of random matrix theory.)] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(High-Dimensional Covariance Matrix Estimation)] TJ ET
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34.016 247.449 m 335.702 247.449 l S
BT 335.702 249.800 Td /F1 14.2 Tf [( Aygul Zagidullina )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(Multivariate Statistics)] TJ ET
BT 167.837 232.401 Td /F1 14.2 Tf [( Yasunori Fujikoshi 2011-08-15 A comprehensive )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(examination of high-dimensional analysis of multivariate methods and their real-)] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(world applications Multivariate Statistics: High-Dimensional and Large-Sample )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(Approximations is the first book of its kind to explore how classical multivariate )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(methods can be revised and used in place of conventional statistical tools. Written )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(by prominent researchers in the field, the book focuses on high-dimensional and )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(large-scale approximations and details the many basic multivariate methods used )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(to achieve high levels of accuracy. The authors begin with a fundamental )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(presentation of the basic tools and exact distributional results of multivariate )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(statistics, and, in addition, the derivations of most distributional results are )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(provided. Statistical methods for high-dimensional data, such as curve data, )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(spectra, images, and DNA microarrays, are discussed. Bootstrap approximations )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(from a methodological point of view, theoretical accuracies in MANOVA tests, and )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(model selection criteria are also presented. Subsequent chapters feature )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(additional topical coverage including: High-dimensional approximations of various )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(statistics High-dimensional statistical methods Approximations with computable )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(error bound Selection of variables based on model selection approach Statistics )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(with error bounds and their appearance in discriminant analysis, growth curve )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(models, generalized linear models, profile analysis, and multiple comparison Each )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(chapter provides real-world applications and thorough analyses of the real data. In )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(addition, approximation formulas found throughout the book are a useful tool for )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(both practical and theoretical statisticians, and basic results on exact distributions )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(in multivariate analysis are included in a comprehensive, yet accessible, format. )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(Multivariate Statistics is an excellent book for courses on probability theory in )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(statistics at the graduate level. It is also an essential reference for both practical )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(and theoretical statisticians who are interested in multivariate analysis and who )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(would benefit from learning the applications of analytical probabilistic methods in )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(statistics.)] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(Fundamentals of High-Dimensional Statistics)] TJ ET
BT 317.519 75.808 Td /F1 14.2 Tf [( Johannes Lederer 2021-11-16 This )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(textbook provides a step-by-step introduction to the tools and principles of high-)] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(dimensional statistics. Each chapter is complemented by numerous exercises, )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(many of them with detailed solutions, and computer labs in R that convey valuable )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(practical insights. The book covers the theory and practice of high-dimensional )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(linear regression, graphical models, and inference, ensuring readers have a )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(smooth start in the field. It also offers suggestions for further reading. Given its )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(scope, the textbook is intended for beginning graduate and advanced )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(undergraduate students in statistics, biostatistics, and bioinformatics, though it will )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(be equally useful to a broader audience.)] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(Statistical Inference from High Dimensional Data)] TJ ET
BT 340.490 232.401 Td /F1 14.2 Tf [( Carlos Fernandez-Lozano 2021-)] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(04-28 • Real-world problems can be high-dimensional, complex, and noisy • More )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(data does not imply more information • Different approaches deal with the so-)] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(called curse of dimensionality to reduce irrelevant information • A process with )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(multidimensional information is not necessarily easy to interpret nor process • In )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(some real-world applications, the number of elements of a class is clearly lower )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(than the other. The models tend to assume that the importance of the analysis )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(belongs to the majority class and this is not usually the truth • The analysis of )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(complex diseases such as cancer are focused on more-than-one dimensional omic )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(data • The increasing amount of data thanks to the reduction of cost of the high-)] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(throughput experiments opens up a new era for integrative data-driven approaches )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(• Entropy-based approaches are of interest to reduce the dimensionality of high-)] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(dimensional data)] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(Applied Biclustering Methods for Big and High-Dimensional Data Using R)] TJ ET
BT 495.716 336.796 Td /F1 14.2 Tf [( Adetayo )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(Kasim 2016-10-03 Proven Methods for Big Data Analysis As big data has become )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(standard in many application areas, challenges have arisen related to )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(methodology and software development, including how to discover meaningful )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(patterns in the vast amounts of data. Addressing these problems, Applied )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(Biclustering Methods for Big and High-Dimensional Data Using R shows how to )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(apply biclustering methods to find local patterns in a big data matrix. The book )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(presents an overview of data analysis using biclustering methods from a practical )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(point of view. Real case studies in drug discovery, genetics, marketing research, )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(biology, toxicity, and sports illustrate the use of several biclustering methods. )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(References to technical details of the methods are provided for readers who wish )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(to investigate the full theoretical background. All the methods are accompanied )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(with R examples that show how to conduct the analyses. The examples, software, )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(and other materials are available on a supplementary website.)] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(Sampling Theory and Practice)] TJ ET
BT 224.880 93.207 Td /F1 14.2 Tf [( Changbao Wu 2020-05-15 The three parts of this )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(book on survey methodology combine an introduction to basic sampling theory, )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(engaging presentation of topics that reflect current research trends, and informed )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(discussion of the problems commonly encountered in survey practice. These )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(related aspects of survey methodology rarely appear together under a single )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(connected roof, making this book a unique combination of materials for teaching, )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(research and practice in survey sampling. Basic knowledge of probability theory )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(and statistical inference is assumed, but no prior exposure to survey sampling is )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(required. The first part focuses on the design-based approach to finite population )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(sampling. It contains a rigorous coverage of basic sampling designs, related )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(estimation theory, model-based prediction approach, and model-assisted )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(estimation methods. The second part stems from original research conducted by )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(the authors as well as important methodological advances in the field during the )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(past three decades. Topics include calibration weighting methods, regression )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(analysis and survey weighted estimating equation \(EE\) theory, longitudinal surveys )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(and generalized estimating equations \(GEE\) analysis, variance estimation and )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(resampling techniques, empirical likelihood methods for complex surveys, handling )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(missing data and non-response, and Bayesian inference for survey data. The third )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(part provides guidance and tools on practical aspects of large-scale surveys, such )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(as training and quality control, frame construction, choices of survey designs, )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(strategies for reducing non-response, and weight calculation. These procedures )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(are illustrated through real-world surveys. Several specialized topics are also )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(discussed in detail, including household surveys, telephone and web surveys, )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(natural resource inventory surveys, adaptive and network surveys, dual-frame and )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(multiple frame surveys, and analysis of non-probability survey samples. This book )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(is a self-contained introduction to survey sampling that provides a strong )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(theoretical base with coverage of current research trends and pragmatic guidance )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(and tools for conducting surveys.)] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(Mathematical Foundations of Infinite-Dimensional Statistical Models)] TJ ET
BT 460.874 267.199 Td /F1 14.2 Tf [( Evarist Giné )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(2021-03-25 In nonparametric and high-dimensional statistical models, the classical )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood estimators )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(and Bayesian posterior inference does not apply, and new foundations and ideas )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(have been developed in the past several decades. This book gives a coherent )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(account of the statistical theory in infinite-dimensional parameter spaces. The )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(mathematical foundations include self-contained 'mini-courses' on the theory of )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(Gaussian and empirical processes, approximation and wavelet theory, and the )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(basic theory of function spaces. The theory of statistical inference in such models - )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(hypothesis testing, estimation and confidence sets - is presented within the )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(minimax paradigm of decision theory. This includes the basic theory of convolution )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(kernel and projection estimation, but also Bayesian nonparametrics and )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(nonparametric maximum likelihood estimation. In a final chapter the theory of )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(adaptive inference in nonparametric models is developed, including Lepski's )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(method, wavelet thresholding, and adaptive inference for self-similar functions. )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(Winner of the 2017 PROSE Award for Mathematics.)] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(High-dimensional Microarray Data Analysis)] TJ ET
BT 306.404 319.397 Td /F1 14.2 Tf [( Shuichi Shinmura 2019-05-24 This )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(book shows how to decompose high-dimensional microarrays into small )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(subspaces \(Small Matryoshkas, SMs\), statistically analyze them, and perform )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(cancer gene diagnosis. The information is useful for genetic experts, anyone who )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(analyzes genetic data, and students to use as practical textbooks. Discriminant )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(analysis is the best approach for microarray consisting of normal and cancer )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(classes. Microarrays are linearly separable data \(LSD, Fact 3\). However, because )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(most linear discriminant function \(LDF\) cannot discriminate LSD theoretically and )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(error rates are high, no one had discovered Fact 3 until now. Hard-margin SVM \(H-)] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(SVM\) and Revised IP-OLDF \(RIP\) can find Fact3 easily. LSD has the Matryoshka )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(structure and is easily decomposed into many SMs \(Fact 4\). Because all SMs are )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(small samples and LSD, statistical methods analyze SMs easily. However, useful )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(results cannot be obtained. On the other hand, H-SVM and RIP can discriminate )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(two classes in SM entirely. RatioSV is the ratio of SV distance and discriminant )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(range. The maximum RatioSVs of six microarrays is over 11.67%. This fact shows )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(that SV separates two classes by window width \(11.67%\). Such easy )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(discrimination has been unresolved since 1970. The reason is revealed by facts )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(presented here, so this book can be read and enjoyed like a mystery novel. Many )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(studies point out that it is difficult to separate signal and noise in a high-)] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(dimensional gene space. However, the definition of the signal is not clear. )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(Convincing evidence is presented that LSD is a signal. Statistical analysis of the )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(genes contained in the SM cannot provide useful information, but it shows that the )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(discriminant score \(DS\) discriminated by RIP or H-SVM is easily LSD. For )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(example, the Alon microarray has 2,000 genes which can be divided into 66 SMs. )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(If 66 DSs are used as variables, the result is a 66-dimensional data. These signal )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(data can be analyzed to find malignancy indicators by principal component )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(analysis and cluster analysis.)] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(Large Sample Covariance Matrices and High-Dimensional Data Analysis)] TJ ET
BT 491.740 180.203 Td /F1 14.2 Tf [( Jianfeng )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(Yao 2015-03-26 High-dimensional data appear in many fields, and their analysis )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(has become increasingly important in modern statistics. However, it has long been )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(observed that several well-known methods in multivariate analysis become )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(inefficient, or even misleading, when the data dimension p is larger than, say, )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(several tens. A seminal example is the well-known inefficiency of Hotelling's T2-)] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(test in such cases. This example shows that classical large sample limits may no )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(longer hold for high-dimensional data; statisticians must seek new limiting )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(theorems in these instances. Thus, the theory of random matrices \(RMT\) serves )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(as a much-needed and welcome alternative framework. Based on the authors' own )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(research, this book provides a first-hand introduction to new high-dimensional )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(statistical methods derived from RMT. The book begins with a detailed introduction )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(to useful tools from RMT, and then presents a series of high-dimensional problems )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(with solutions provided by RMT methods.)] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(Statistical Foundations of Data Science)] TJ ET
BT 281.923 267.199 Td /F1 14.2 Tf [( Jianqing Fan 2020-09-21 Statistical )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(Foundations of Data Science gives a thorough introduction to commonly used )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(statistical models, contemporary statistical machine learning techniques and )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(algorithms, along with their mathematical insights and statistical theories. It aims to )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(serve as a graduate-level textbook and a research monograph on high-)] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(dimensional statistics, sparsity and covariance learning, machine learning, and )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(statistical inference. It includes ample exercises that involve both theoretical )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(studies as well as empirical applications. The book begins with an introduction to )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(the stylized features of big data and their impacts on statistical analysis. It then )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(introduces multiple linear regression and expands the techniques of model building )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(via nonparametric regression and kernel tricks. It provides a comprehensive )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(account on sparsity explorations and model selections for multiple regression, )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(generalized linear models, quantile regression, robust regression, hazards )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(regression, among others. High-dimensional inference is also thoroughly )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(addressed and so is feature screening. The book also provides a comprehensive )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(account on high-dimensional covariance estimation, learning latent factors and )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(hidden structures, as well as their applications to statistical estimation, inference, )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(prediction and machine learning problems. It also introduces thoroughly statistical )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(machine learning theory and methods for classification, clustering, and prediction. )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(These include CART, random forests, boosting, support vector machines, )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(clustering algorithms, sparse PCA, and deep learning.)] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(Elements of Large-Sample Theory)] TJ ET
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BT 251.015 232.401 Td /F1 14.2 Tf [( E.L. Lehmann 2006-04-18 Written by one of the )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(main figures in twentieth century statistics, this book provides a unified treatment )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(of first-order large-sample theory. It discusses a broad range of applications )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(including introductions to density estimation, the bootstrap, and the asymptotics of )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(survey methodology. The book is written at an elementary level making it )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(accessible to most readers.)] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(High-dimensional Statistics)] TJ ET
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BT 205.058 128.005 Td /F1 14.2 Tf [( Johannes Lederer 2020 "An Introduction to )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(Regularized Estimation in High Dimensions considers statistical theory, methods, )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(and algorithms for large and complex data. The main focus is on regularized )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(estimators, which are at the cusp of entering the statistical toolkits of almost all )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(scientific disciplines. This book provides clear expositions, motivational )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(introductions to each chapter, rigorous step-by-step proofs, and comprehensive )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(exercise sets with fully worked out solutions. These features make this book ideal )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(for graduate level courses. Moreover, the book also discusses cutting-edge topics, )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(such as aspects of inference, robustness, and tuning parameters. The book also )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(contains results and insights that are new altogether, including improvements of )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(existing theories and novel connections among different methods, which are )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(organized into special chapters for those wishing to advance their reading in the )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(field."--)] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(Bayesian and High-Dimensional Global Optimization)] TJ ET
BT 365.043 232.401 Td /F1 14.2 Tf [( Anatoly Zhigljavsky 2021-03-)] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(02 Accessible to a variety of readers, this book is of interest to specialists, )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(graduate students and researchers in mathematics, optimization, computer )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(science, operations research, management science, engineering and other applied )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(areas interested in solving optimization problems. Basic principles, potential and )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(boundaries of applicability of stochastic global optimization techniques are )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(examined in this book. A variety of issues that face specialists in global )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(optimization are explored, such as multidimensional spaces which are frequently )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(ignored by researchers. The importance of precise interpretation of the )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(mathematical results in assessments of optimization methods is demonstrated )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(through examples of convergence in probability of random search. Methodological )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(issues concerning construction and applicability of stochastic global optimization )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(methods are discussed, including the one-step optimal average improvement )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(method based on a statistical model of the objective function. A significant portion )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(of this book is devoted to an analysis of high-dimensional global optimization )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(problems and the so-called ‘curse of dimensionality’. An examination of the three )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(different classes of high-dimensional optimization problems, the geometry of high-)] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(dimensional balls and cubes, very slow convergence of global random search )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(algorithms in large-dimensional problems , and poor uniformity of the uniformly )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(distributed sequences of points are included in this book.)] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(Foundations of Data Science)] TJ ET
BT 216.986 215.002 Td /F1 14.2 Tf [( Avrim Blum 2020-01-23 This book provides an )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(introduction to the mathematical and algorithmic foundations of data science, )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(including machine learning, high-dimensional geometry, and analysis of large )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(networks. Topics include the counterintuitive nature of data in high dimensions, )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(important linear algebraic techniques such as singular value decomposition, the )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(theory of random walks and Markov chains, the fundamentals of and important )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(algorithms for machine learning, algorithms and analysis for clustering, )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(probabilistic models for large networks, representation learning including topic )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(modelling and non-negative matrix factorization, wavelets and compressed )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(sensing. Important probabilistic techniques are developed including the law of )] TJ ET
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BT 34.016 371.595 Td /F1 14.2 Tf [(large numbers, tail inequalities, analysis of random projections, generalization )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(guarantees in machine learning, and moment methods for analysis of phase )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(transitions in large random graphs. Additionally, important structural and )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(complexity measures are discussed such as matrix norms and VC-dimension. This )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(book is suitable for both undergraduate and graduate courses in the design and )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(analysis of algorithms for data.)] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(High-Dimensional Covariance Estimation)] TJ ET
BT 292.952 267.199 Td /F1 14.2 Tf [( Mohsen Pourahmadi 2013-06-24 )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [("Focusing on methodology and computation more than on theorems and proofs, )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(this book provides computationally feasible and statistically efficient methods for )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(estimating sparse and large covariance matrices of high-dimensional data. )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(Extensive in breadth and scope, it features ample applications to a number of )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(applied areas, including business and economics, computer science, engineering, )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(and financial mathematics; recognizes the important and significant contributions )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(of longitudinal and spatial data; and includes various computer codes in R )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(throughout the text and on an author-maintained web site"--)] TJ ET
BT 36.266 83.111 Td /F1 8.0 Tf [(statistics-for-high-dimensional-data-methods-theory-and-applications)] TJ ET
BT 336.238 83.318 Td /F1 8.0 Tf [(Downloaded from )] TJ ET
BT 401.150 83.111 Td /F1 8.0 Tf [(zurcoin.org)] TJ ET
BT 440.718 83.318 Td /F1 8.0 Tf [( on September 27, 2022 by guest)] TJ ET
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