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BT 34.016 536.562 Td /F1 15.0 Tf [(Statistics For High Dimensional Data Methods )] TJ ET
BT 34.016 518.247 Td /F1 15.0 Tf [(Theory And Applications)] TJ ET
BT 34.016 489.708 Td /F1 7.5 Tf [(Right here, we have countless ebook )] TJ ET
BT 159.078 489.708 Td /F1 7.5 Tf [(Statistics For High Dimensional Data Methods Theory And )] TJ ET
BT 34.016 480.551 Td /F1 7.5 Tf [(Applications)] TJ ET
BT 74.448 480.551 Td /F1 7.5 Tf [( and collections to check out. We additionally pay for variant types and plus type of the books )] TJ ET
BT 34.016 471.393 Td /F1 7.5 Tf [(to browse. The normal book, fiction, history, novel, scientific research, as well as various extra sorts of )] TJ ET
BT 34.016 462.236 Td /F1 7.5 Tf [(books are readily to hand here. )] TJ ET
BT 34.016 444.078 Td /F1 7.5 Tf [(As this Statistics For High Dimensional Data Methods Theory And Applications, it ends occurring living )] TJ ET
BT 34.016 434.921 Td /F1 7.5 Tf [(thing one of the favored ebook Statistics For High Dimensional Data Methods Theory And Applications )] TJ ET
BT 34.016 425.763 Td /F1 7.5 Tf [(collections that we have. This is why you remain in the best website to look the incredible books to have.)] TJ ET
BT 34.016 392.606 Td /F1 7.5 Tf [(Bayesian and High-Dimensional Global Optimization)] TJ ET
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BT 208.241 392.606 Td /F1 7.5 Tf [( Anatoly Zhigljavsky 2021-03-02 Accessible to a )] TJ ET
BT 34.016 383.448 Td /F1 7.5 Tf [(variety of readers, this book is of interest to specialists, graduate students and researchers in )] TJ ET
BT 34.016 374.291 Td /F1 7.5 Tf [(mathematics, optimization, computer science, operations research, management science, engineering )] TJ ET
BT 34.016 365.133 Td /F1 7.5 Tf [(and other applied areas interested in solving optimization problems. Basic principles, potential and )] TJ ET
BT 34.016 355.976 Td /F1 7.5 Tf [(boundaries of applicability of stochastic global optimization techniques are examined in this book. A )] TJ ET
BT 34.016 346.818 Td /F1 7.5 Tf [(variety of issues that face specialists in global optimization are explored, such as multidimensional )] TJ ET
BT 34.016 337.661 Td /F1 7.5 Tf [(spaces which are frequently ignored by researchers. The importance of precise interpretation of the )] TJ ET
BT 34.016 328.503 Td /F1 7.5 Tf [(mathematical results in assessments of optimization methods is demonstrated through examples of )] TJ ET
BT 34.016 319.346 Td /F1 7.5 Tf [(convergence in probability of random search. Methodological issues concerning construction and )] TJ ET
BT 34.016 310.188 Td /F1 7.5 Tf [(applicability of stochastic global optimization methods are discussed, including the one-step optimal )] TJ ET
BT 34.016 301.031 Td /F1 7.5 Tf [(average improvement method based on a statistical model of the objective function. A significant portion )] TJ ET
BT 34.016 291.873 Td /F1 7.5 Tf [(of this book is devoted to an analysis of high-dimensional global optimization problems and the so-called )] TJ ET
BT 34.016 282.716 Td /F1 7.5 Tf [(‘curse of dimensionality’. An examination of the three different classes of high-dimensional optimization )] TJ ET
BT 34.016 273.558 Td /F1 7.5 Tf [(problems, the geometry of high-dimensional balls and cubes, very slow convergence of global random )] TJ ET
BT 34.016 264.401 Td /F1 7.5 Tf [(search algorithms in large-dimensional problems , and poor uniformity of the uniformly distributed )] TJ ET
BT 34.016 255.243 Td /F1 7.5 Tf [(sequences of points are included in this book.)] TJ ET
BT 34.016 246.086 Td /F1 7.5 Tf [(Statistical Inference from High Dimensional Data)] TJ ET
BT 195.318 246.086 Td /F1 7.5 Tf [( Carlos Fernandez-Lozano 2021-04-28 • Real-world )] TJ ET
BT 34.016 236.928 Td /F1 7.5 Tf [(problems can be high-dimensional, complex, and noisy • More data does not imply more information • )] TJ ET
BT 34.016 227.771 Td /F1 7.5 Tf [(Different approaches deal with the so-called curse of dimensionality to reduce irrelevant information • A )] TJ ET
BT 34.016 218.613 Td /F1 7.5 Tf [(process with multidimensional information is not necessarily easy to interpret nor process • In some real-)] TJ ET
BT 34.016 209.456 Td /F1 7.5 Tf [(world applications, the number of elements of a class is clearly lower than the other. The models tend to )] TJ ET
BT 34.016 200.298 Td /F1 7.5 Tf [(assume that the importance of the analysis belongs to the majority class and this is not usually the truth • )] TJ ET
BT 34.016 191.141 Td /F1 7.5 Tf [(The analysis of complex diseases such as cancer are focused on more-than-one dimensional omic data • )] TJ ET
BT 34.016 181.983 Td /F1 7.5 Tf [(The increasing amount of data thanks to the reduction of cost of the high-throughput experiments opens )] TJ ET
BT 34.016 172.826 Td /F1 7.5 Tf [(up a new era for integrative data-driven approaches • Entropy-based approaches are of interest to )] TJ ET
BT 34.016 163.668 Td /F1 7.5 Tf [(reduce the dimensionality of high-dimensional data)] TJ ET
BT 34.016 154.511 Td /F1 7.5 Tf [(Statistical Models)] TJ ET
BT 92.366 154.511 Td /F1 7.5 Tf [( David A. Freedman 2009-04-27 This lively and engaging book explains the things you )] TJ ET
BT 34.016 145.353 Td /F1 7.5 Tf [(have to know in order to read empirical papers in the social and health sciences, as well as the )] TJ ET
BT 34.016 136.196 Td /F1 7.5 Tf [(techniques you need to build statistical models of your own. The discussion in the book is organized )] TJ ET
BT 34.016 127.038 Td /F1 7.5 Tf [(around published studies, as are many of the exercises. Relevant journal articles are reprinted at the )] TJ ET
BT 34.016 117.881 Td /F1 7.5 Tf [(back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in )] TJ ET
BT 34.016 108.723 Td /F1 7.5 Tf [(a variety of other examples. He illustrates the principles of modelling, and the pitfalls. The discussion )] TJ ET
BT 34.016 99.566 Td /F1 7.5 Tf [(shows you how to think about the critical issues - including the connection \(or lack of it\) between the )] TJ ET
BT 34.016 90.408 Td /F1 7.5 Tf [(statistical models and the real phenomena. The book is written for advanced undergraduates and )] TJ ET
BT 34.016 81.251 Td /F1 7.5 Tf [(beginning graduate students in statistics, as well as students and professionals in the social and health )] TJ ET
BT 34.016 72.093 Td /F1 7.5 Tf [(sciences.)] TJ ET
BT 34.016 62.936 Td /F1 7.5 Tf [(Statistical Learning with Sparsity)] TJ ET
BT 141.971 62.936 Td /F1 7.5 Tf [( Trevor Hastie 2015-05-07 Discover New Methods for Dealing with High-)] TJ ET
BT 34.016 53.778 Td /F1 7.5 Tf [(Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; )] TJ ET
BT 34.016 44.621 Td /F1 7.5 Tf [(therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with )] TJ ET
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BT 34.016 553.938 Td /F1 7.5 Tf [(Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the )] TJ ET
BT 34.016 544.781 Td /F1 7.5 Tf [(underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso )] TJ ET
BT 34.016 535.623 Td /F1 7.5 Tf [(for linear regression and a simple coordinate descent algorithm for its computation. They discuss the )] TJ ET
BT 34.016 526.466 Td /F1 7.5 Tf [(application of l1 penalties to generalized linear models and support vector machines, cover generalized )] TJ ET
BT 34.016 517.308 Td /F1 7.5 Tf [(penalties such as the elastic net and group lasso, and review numerical methods for optimization. They )] TJ ET
BT 34.016 508.151 Td /F1 7.5 Tf [(also present statistical inference methods for fitted \(lasso\) models, including the bootstrap, Bayesian )] TJ ET
BT 34.016 498.993 Td /F1 7.5 Tf [(methods, and recently developed approaches. In addition, the book examines matrix decomposition, )] TJ ET
BT 34.016 489.836 Td /F1 7.5 Tf [(sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of )] TJ ET
BT 34.016 480.678 Td /F1 7.5 Tf [(theoretical results for the lasso. In this age of big data, the number of features measured on a person or )] TJ ET
BT 34.016 471.521 Td /F1 7.5 Tf [(object can be large and might be larger than the number of observations. This book shows how the )] TJ ET
BT 34.016 462.363 Td /F1 7.5 Tf [(sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from )] TJ ET
BT 34.016 453.206 Td /F1 7.5 Tf [(big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-)] TJ ET
BT 34.016 444.048 Td /F1 7.5 Tf [(date treatment of sparse statistical modeling.)] TJ ET
BT 34.016 434.891 Td /F1 7.5 Tf [(Multivariate Statistics)] TJ ET
BT 104.448 434.891 Td /F1 7.5 Tf [( Yasunori Fujikoshi 2011-08-15 A comprehensive examination of high-dimensional )] TJ ET
BT 34.016 425.733 Td /F1 7.5 Tf [(analysis of multivariate methods and their real-world applications Multivariate Statistics: High-)] TJ ET
BT 34.016 416.576 Td /F1 7.5 Tf [(Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical )] TJ ET
BT 34.016 407.418 Td /F1 7.5 Tf [(multivariate methods can be revised and used in place of conventional statistical tools. Written by )] TJ ET
BT 34.016 398.261 Td /F1 7.5 Tf [(prominent researchers in the field, the book focuses on high-dimensional and large-scale approximations )] TJ ET
BT 34.016 389.103 Td /F1 7.5 Tf [(and details the many basic multivariate methods used to achieve high levels of accuracy. The authors )] TJ ET
BT 34.016 379.946 Td /F1 7.5 Tf [(begin with a fundamental presentation of the basic tools and exact distributional results of multivariate )] TJ ET
BT 34.016 370.788 Td /F1 7.5 Tf [(statistics, and, in addition, the derivations of most distributional results are provided. Statistical methods )] TJ ET
BT 34.016 361.631 Td /F1 7.5 Tf [(for high-dimensional data, such as curve data, spectra, images, and DNA microarrays, are discussed. )] TJ ET
BT 34.016 352.473 Td /F1 7.5 Tf [(Bootstrap approximations from a methodological point of view, theoretical accuracies in MANOVA tests, )] TJ ET
BT 34.016 343.316 Td /F1 7.5 Tf [(and model selection criteria are also presented. Subsequent chapters feature additional topical coverage )] TJ ET
BT 34.016 334.158 Td /F1 7.5 Tf [(including: High-dimensional approximations of various statistics High-dimensional statistical methods )] TJ ET
BT 34.016 325.001 Td /F1 7.5 Tf [(Approximations with computable error bound Selection of variables based on model selection approach )] TJ ET
BT 34.016 315.843 Td /F1 7.5 Tf [(Statistics with error bounds and their appearance in discriminant analysis, growth curve models, )] TJ ET
BT 34.016 306.686 Td /F1 7.5 Tf [(generalized linear models, profile analysis, and multiple comparison Each chapter provides real-world )] TJ ET
BT 34.016 297.528 Td /F1 7.5 Tf [(applications and thorough analyses of the real data. In addition, approximation formulas found throughout )] TJ ET
BT 34.016 288.371 Td /F1 7.5 Tf [(the book are a useful tool for both practical and theoretical statisticians, and basic results on exact )] TJ ET
BT 34.016 279.213 Td /F1 7.5 Tf [(distributions in multivariate analysis are included in a comprehensive, yet accessible, format. Multivariate )] TJ ET
BT 34.016 270.056 Td /F1 7.5 Tf [(Statistics is an excellent book for courses on probability theory in statistics at the graduate level. It is also )] TJ ET
BT 34.016 260.898 Td /F1 7.5 Tf [(an essential reference for both practical and theoretical statisticians who are interested in multivariate )] TJ ET
BT 34.016 251.741 Td /F1 7.5 Tf [(analysis and who would benefit from learning the applications of analytical probabilistic methods in )] TJ ET
BT 34.016 242.583 Td /F1 7.5 Tf [(statistics.)] TJ ET
BT 34.016 233.426 Td /F1 7.5 Tf [(Applied Biclustering Methods for Big and High-Dimensional Data Using R)] TJ ET
BT 277.016 233.426 Td /F1 7.5 Tf [( Adetayo Kasim 2016-10-03 )] TJ ET
BT 34.016 224.268 Td /F1 7.5 Tf [(Proven Methods for Big Data Analysis As big data has become standard in many application areas, )] TJ ET
BT 34.016 215.111 Td /F1 7.5 Tf [(challenges have arisen related to methodology and software development, including how to discover )] TJ ET
BT 34.016 205.953 Td /F1 7.5 Tf [(meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering )] TJ ET
BT 34.016 196.796 Td /F1 7.5 Tf [(Methods for Big and High-Dimensional Data Using R shows how to apply biclustering methods to find )] TJ ET
BT 34.016 187.638 Td /F1 7.5 Tf [(local patterns in a big data matrix. The book presents an overview of data analysis using biclustering )] TJ ET
BT 34.016 178.481 Td /F1 7.5 Tf [(methods from a practical point of view. Real case studies in drug discovery, genetics, marketing )] TJ ET
BT 34.016 169.323 Td /F1 7.5 Tf [(research, biology, toxicity, and sports illustrate the use of several biclustering methods. References to )] TJ ET
BT 34.016 160.166 Td /F1 7.5 Tf [(technical details of the methods are provided for readers who wish to investigate the full theoretical )] TJ ET
BT 34.016 151.008 Td /F1 7.5 Tf [(background. All the methods are accompanied with R examples that show how to conduct the analyses. )] TJ ET
BT 34.016 141.851 Td /F1 7.5 Tf [(The examples, software, and other materials are available on a supplementary website.)] TJ ET
BT 34.016 132.693 Td /F1 7.5 Tf [(Introduction to High-Dimensional Statistics)] TJ ET
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34.016 131.456 m 174.896 131.456 l S
BT 174.896 132.693 Td /F1 7.5 Tf [( Christophe Giraud 2014-12-17 Ever-greater computing )] TJ ET
BT 34.016 123.536 Td /F1 7.5 Tf [(technologies have given rise to an exponentially growing volume of data. Today massive data sets \(with )] TJ ET
BT 34.016 114.378 Td /F1 7.5 Tf [(potentially thousands of variables\) play an important role in almost every branch of modern human )] TJ ET
BT 34.016 105.221 Td /F1 7.5 Tf [(activity, including networks, finance, and genetics. However, analyzing such data has presented a )] TJ ET
BT 34.016 96.063 Td /F1 7.5 Tf [(challenge for statisticians and data analysts and has required the development of new statistical methods )] TJ ET
BT 34.016 86.906 Td /F1 7.5 Tf [(capable of separating the signal from the noise. Introduction to High-Dimensional Statistics is a concise )] TJ ET
BT 34.016 77.748 Td /F1 7.5 Tf [(guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The )] TJ ET
BT 34.016 68.591 Td /F1 7.5 Tf [(book is intended to expose the reader to the key concepts and ideas in the most simple settings possible )] TJ ET
BT 34.016 59.433 Td /F1 7.5 Tf [(while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical )] TJ ET
BT 34.016 50.276 Td /F1 7.5 Tf [(foundations of high-dimensional statistics, this highly accessible text: Describes the challenges related to )] TJ ET
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BT 34.016 553.938 Td /F1 7.5 Tf [(the analysis of high-dimensional data Covers cutting-edge statistical methods including model selection, )] TJ ET
BT 34.016 544.781 Td /F1 7.5 Tf [(sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every )] TJ ET
BT 34.016 535.623 Td /F1 7.5 Tf [(chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical )] TJ ET
BT 34.016 526.466 Td /F1 7.5 Tf [(examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers )] TJ ET
BT 34.016 517.308 Td /F1 7.5 Tf [(interested in discovering modern statistics for massive data. It can be used as a graduate text or for self-)] TJ ET
BT 34.016 508.151 Td /F1 7.5 Tf [(study.)] TJ ET
BT 34.016 498.993 Td /F1 7.5 Tf [(Statistical Analysis for High-Dimensional Data)] TJ ET
BT 186.138 498.993 Td /F1 7.5 Tf [( Arnoldo Frigessi 2016-02-16 This book features research )] TJ ET
BT 34.016 489.836 Td /F1 7.5 Tf [(contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in )] TJ ET
BT 34.016 480.678 Td /F1 7.5 Tf [(Nyvågar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine )] TJ ET
BT 34.016 471.521 Td /F1 7.5 Tf [(learning methodologies specifically developed for inference in “big data” situations, with particular )] TJ ET
BT 34.016 462.363 Td /F1 7.5 Tf [(reference to genomic applications. The contributors, who are among the most prominent researchers on )] TJ ET
BT 34.016 453.206 Td /F1 7.5 Tf [(the theory of statistics for high dimensional inference, present new theories and methods, as well as )] TJ ET
BT 34.016 444.048 Td /F1 7.5 Tf [(challenging applications and computational solutions. Specific themes include, among others, variable )] TJ ET
BT 34.016 434.891 Td /F1 7.5 Tf [(selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, )] TJ ET
BT 34.016 425.733 Td /F1 7.5 Tf [(computational challenges, non-convex situations, learning graphical models, sparse covariance and )] TJ ET
BT 34.016 416.576 Td /F1 7.5 Tf [(precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, )] TJ ET
BT 34.016 407.418 Td /F1 7.5 Tf [(clustering, and preselection. Highlighting cutting-edge research and casting light on future research )] TJ ET
BT 34.016 398.261 Td /F1 7.5 Tf [(directions, the contributions will benefit graduate students and researchers in computational biology, )] TJ ET
BT 34.016 389.103 Td /F1 7.5 Tf [(statistics and the machine learning community.)] TJ ET
BT 34.016 379.946 Td /F1 7.5 Tf [(High-dimensional Data Analysis)] TJ ET
BT 139.878 379.946 Td /F1 7.5 Tf [( Tianwen Tony Cai 2011 Over the last few years, significant )] TJ ET
BT 34.016 370.788 Td /F1 7.5 Tf [(developments have been taking place in high-dimensional data analysis, driven primarily by a wide range )] TJ ET
BT 34.016 361.631 Td /F1 7.5 Tf [(of applications in many fields such as genomics and signal processing. In particular, substantial )] TJ ET
BT 34.016 352.473 Td /F1 7.5 Tf [(advances have been made in the areas of feature selection, covariance estimation, classification and )] TJ ET
BT 34.016 343.316 Td /F1 7.5 Tf [(regression. This book intends to examine important issues arising from high-dimensional data analysis to )] TJ ET
BT 34.016 334.158 Td /F1 7.5 Tf [(explore key ideas for statistical inference and prediction. It is structured around topics on multiple )] TJ ET
BT 34.016 325.001 Td /F1 7.5 Tf [(hypothesis testing, feature selection, regression, classification, dimension reduction, as well as )] TJ ET
BT 34.016 315.843 Td /F1 7.5 Tf [(applications in survival analysis and biomedical research. The book will appeal to graduate students and )] TJ ET
BT 34.016 306.686 Td /F1 7.5 Tf [(new researchers interested in the plethora of opportunities available in high-dimensional data analysis.)] TJ ET
BT 34.016 297.528 Td /F1 7.5 Tf [(Model-Based Clustering and Classification for Data Science)] TJ ET
0.15 w 0 J [ ] 0 d
34.016 296.291 m 232.421 296.291 l S
BT 232.421 297.528 Td /F1 7.5 Tf [( Charles Bouveyron 2019-07-25 Colorful )] TJ ET
BT 34.016 288.371 Td /F1 7.5 Tf [(example-rich introduction to the state-of-the-art for students in data science, as well as researchers and )] TJ ET
BT 34.016 279.213 Td /F1 7.5 Tf [(practitioners.)] TJ ET
BT 34.016 270.056 Td /F1 7.5 Tf [(High-dimensional Microarray Data Analysis)] TJ ET
BT 177.378 270.056 Td /F1 7.5 Tf [( Shuichi Shinmura 2019-05-24 This book shows how to )] TJ ET
BT 34.016 260.898 Td /F1 7.5 Tf [(decompose high-dimensional microarrays into small subspaces \(Small Matryoshkas, SMs\), statistically )] TJ ET
BT 34.016 251.741 Td /F1 7.5 Tf [(analyze them, and perform cancer gene diagnosis. The information is useful for genetic experts, anyone )] TJ ET
BT 34.016 242.583 Td /F1 7.5 Tf [(who analyzes genetic data, and students to use as practical textbooks. Discriminant analysis is the best )] TJ ET
BT 34.016 233.426 Td /F1 7.5 Tf [(approach for microarray consisting of normal and cancer classes. Microarrays are linearly separable data )] TJ ET
BT 34.016 224.268 Td /F1 7.5 Tf [(\(LSD, Fact 3\). However, because most linear discriminant function \(LDF\) cannot discriminate LSD )] TJ ET
BT 34.016 215.111 Td /F1 7.5 Tf [(theoretically and error rates are high, no one had discovered Fact 3 until now. Hard-margin SVM \(H-)] TJ ET
BT 34.016 205.953 Td /F1 7.5 Tf [(SVM\) and Revised IP-OLDF \(RIP\) can find Fact3 easily. LSD has the Matryoshka structure and is easily )] TJ ET
BT 34.016 196.796 Td /F1 7.5 Tf [(decomposed into many SMs \(Fact 4\). Because all SMs are small samples and LSD, statistical methods )] TJ ET
BT 34.016 187.638 Td /F1 7.5 Tf [(analyze SMs easily. However, useful results cannot be obtained. On the other hand, H-SVM and RIP can )] TJ ET
BT 34.016 178.481 Td /F1 7.5 Tf [(discriminate two classes in SM entirely. RatioSV is the ratio of SV distance and discriminant range. The )] TJ ET
BT 34.016 169.323 Td /F1 7.5 Tf [(maximum RatioSVs of six microarrays is over 11.67%. This fact shows that SV separates two classes by )] TJ ET
BT 34.016 160.166 Td /F1 7.5 Tf [(window width \(11.67%\). Such easy discrimination has been unresolved since 1970. The reason is )] TJ ET
BT 34.016 151.008 Td /F1 7.5 Tf [(revealed by facts presented here, so this book can be read and enjoyed like a mystery novel. Many )] TJ ET
BT 34.016 141.851 Td /F1 7.5 Tf [(studies point out that it is difficult to separate signal and noise in a high-dimensional gene space. )] TJ ET
BT 34.016 132.693 Td /F1 7.5 Tf [(However, the definition of the signal is not clear. Convincing evidence is presented that LSD is a signal. )] TJ ET
BT 34.016 123.536 Td /F1 7.5 Tf [(Statistical analysis of the genes contained in the SM cannot provide useful information, but it shows that )] TJ ET
BT 34.016 114.378 Td /F1 7.5 Tf [(the discriminant score \(DS\) discriminated by RIP or H-SVM is easily LSD. For example, the Alon )] TJ ET
BT 34.016 105.221 Td /F1 7.5 Tf [(microarray has 2,000 genes which can be divided into 66 SMs. If 66 DSs are used as variables, the )] TJ ET
BT 34.016 96.063 Td /F1 7.5 Tf [(result is a 66-dimensional data. These signal data can be analyzed to find malignancy indicators by )] TJ ET
BT 34.016 86.906 Td /F1 7.5 Tf [(principal component analysis and cluster analysis.)] TJ ET
BT 34.016 77.748 Td /F1 7.5 Tf [(Introduction to High-Dimensional Statistics)] TJ ET
BT 174.896 77.748 Td /F1 7.5 Tf [( Christophe Giraud 2014-12-17 Ever-greater computing )] TJ ET
BT 34.016 68.591 Td /F1 7.5 Tf [(technologies have given rise to an exponentially growing volume of data. Today massive data sets \(with )] TJ ET
BT 34.016 59.433 Td /F1 7.5 Tf [(potentially thousands of variables\) play an important role in almost every branch of modern human )] TJ ET
BT 34.016 50.276 Td /F1 7.5 Tf [(activity, including networks, finance, and genetics. However, analyzing such data has presented a )] TJ ET
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BT 34.016 553.938 Td /F1 7.5 Tf [(challenge for statisticians and data analysts and has required the development of new statistical methods )] TJ ET
BT 34.016 544.781 Td /F1 7.5 Tf [(capable of separating the signal from the noise. Introduction to High-Dimensional Statistics is a concise )] TJ ET
BT 34.016 535.623 Td /F1 7.5 Tf [(guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The )] TJ ET
BT 34.016 526.466 Td /F1 7.5 Tf [(book is intended to expose the reader to the key concepts and ideas in the most simple settings possible )] TJ ET
BT 34.016 517.308 Td /F1 7.5 Tf [(while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical )] TJ ET
BT 34.016 508.151 Td /F1 7.5 Tf [(foundations of high-dimensional statistics, this highly accessible text: Describes the challenges related to )] TJ ET
BT 34.016 498.993 Td /F1 7.5 Tf [(the analysis of high-dimensional data Covers cutting-edge statistical methods including model selection, )] TJ ET
BT 34.016 489.836 Td /F1 7.5 Tf [(sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every )] TJ ET
BT 34.016 480.678 Td /F1 7.5 Tf [(chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical )] TJ ET
BT 34.016 471.521 Td /F1 7.5 Tf [(examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers )] TJ ET
BT 34.016 462.363 Td /F1 7.5 Tf [(interested in discovering modern statistics for massive data. It can be used as a graduate text or for self-)] TJ ET
BT 34.016 453.206 Td /F1 7.5 Tf [(study.)] TJ ET
BT 34.016 444.048 Td /F1 7.5 Tf [(Statistics for High-Dimensional Data)] TJ ET
BT 154.046 444.048 Td /F1 7.5 Tf [( Peter Bühlmann 2011-06-08 Modern statistics deals with large and )] TJ ET
BT 34.016 434.891 Td /F1 7.5 Tf [(complex data sets, and consequently with models containing a large number of parameters. This book )] TJ ET
BT 34.016 425.733 Td /F1 7.5 Tf [(presents a detailed account of recently developed approaches, including the Lasso and versions of it for )] TJ ET
BT 34.016 416.576 Td /F1 7.5 Tf [(various models, boosting methods, undirected graphical modeling, and procedures controlling false )] TJ ET
BT 34.016 407.418 Td /F1 7.5 Tf [(positive selections. A special characteristic of the book is that it contains comprehensive mathematical )] TJ ET
BT 34.016 398.261 Td /F1 7.5 Tf [(theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real )] TJ ET
BT 34.016 389.103 Td /F1 7.5 Tf [(data examples. This in-depth approach highlights the methods’ great potential and practical applicability )] TJ ET
BT 34.016 379.946 Td /F1 7.5 Tf [(in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts )] TJ ET
BT 34.016 370.788 Td /F1 7.5 Tf [(in statistics, applied mathematics and computer science.)] TJ ET
BT 34.016 361.631 Td /F1 7.5 Tf [(Fundamentals of High-Dimensional Statistics)] TJ ET
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34.016 360.393 m 183.228 360.393 l S
BT 183.228 361.631 Td /F1 7.5 Tf [( Johannes Lederer 2021-11-16 This textbook provides a )] TJ ET
BT 34.016 352.473 Td /F1 7.5 Tf [(step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is )] TJ ET
BT 34.016 343.316 Td /F1 7.5 Tf [(complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that )] TJ ET
BT 34.016 334.158 Td /F1 7.5 Tf [(convey valuable practical insights. The book covers the theory and practice of high-dimensional linear )] TJ ET
BT 34.016 325.001 Td /F1 7.5 Tf [(regression, graphical models, and inference, ensuring readers have a smooth start in the field. It also )] TJ ET
BT 34.016 315.843 Td /F1 7.5 Tf [(offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate )] TJ ET
BT 34.016 306.686 Td /F1 7.5 Tf [(and advanced undergraduate students in statistics, biostatistics, and bioinformatics, though it will be )] TJ ET
BT 34.016 297.528 Td /F1 7.5 Tf [(equally useful to a broader audience.)] TJ ET
BT 34.016 288.371 Td /F1 7.5 Tf [(Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes)] TJ ET
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34.016 287.133 m 332.051 287.133 l S
BT 332.051 288.371 Td /F1 7.5 Tf [( Chihwa Kao )] TJ ET
BT 34.016 279.213 Td /F1 7.5 Tf [(2020-08-24 This book aims to fill the gap between panel data econometrics textbooks, and the latest )] TJ ET
BT 34.016 270.056 Td /F1 7.5 Tf [(development on 'big data', especially large-dimensional panel data econometrics. It introduces important )] TJ ET
BT 34.016 260.898 Td /F1 7.5 Tf [(research questions in large panels, including testing for cross-sectional dependence, estimation of factor-)] TJ ET
BT 34.016 251.741 Td /F1 7.5 Tf [(augmented panel data models, structural breaks in panels and group patterns in panels. To tackle these )] TJ ET
BT 34.016 242.583 Td /F1 7.5 Tf [(high dimensional issues, some techniques used in Machine Learning approaches are also illustrated. )] TJ ET
BT 34.016 233.426 Td /F1 7.5 Tf [(Moreover, the Monte Carlo experiments, and empirical examples are also utilised to show how to )] TJ ET
BT 34.016 224.268 Td /F1 7.5 Tf [(implement these new inference methods. Large-Dimensional Panel Data Econometrics: Testing, )] TJ ET
BT 34.016 215.111 Td /F1 7.5 Tf [(Estimation and Structural Changes also introduces new research questions and results in recent )] TJ ET
BT 34.016 205.953 Td /F1 7.5 Tf [(literature in this field.)] TJ ET
BT 34.016 196.796 Td /F1 7.5 Tf [(Functional and High-Dimensional Statistics and Related Fields)] TJ ET
BT 241.173 196.796 Td /F1 7.5 Tf [( Germán Aneiros 2020-06-19 This book )] TJ ET
BT 34.016 187.638 Td /F1 7.5 Tf [(presents the latest research on the statistical analysis of functional, high-dimensional and other complex )] TJ ET
BT 34.016 178.481 Td /F1 7.5 Tf [(data, addressing methodological and computational aspects, as well as real-world applications. It covers )] TJ ET
BT 34.016 169.323 Td /F1 7.5 Tf [(topics like classification, confidence bands, density estimation, depth, diagnostic tests, dimension )] TJ ET
BT 34.016 160.166 Td /F1 7.5 Tf [(reduction, estimation on manifolds, high- and infinite-dimensional statistics, inference on functional data, )] TJ ET
BT 34.016 151.008 Td /F1 7.5 Tf [(networks, operatorial statistics, prediction, regression, robustness, sequential learning, small-ball )] TJ ET
BT 34.016 141.851 Td /F1 7.5 Tf [(probability, smoothing, spatial data, testing, and topological object data analysis, and includes )] TJ ET
BT 34.016 132.693 Td /F1 7.5 Tf [(applications in automobile engineering, criminology, drawing recognition, economics, environmetrics, )] TJ ET
BT 34.016 123.536 Td /F1 7.5 Tf [(medicine, mobile phone data, spectrometrics and urban environments. The book gathers selected, )] TJ ET
BT 34.016 114.378 Td /F1 7.5 Tf [(refereed contributions presented at the Fifth International Workshop on Functional and Operatorial )] TJ ET
BT 34.016 105.221 Td /F1 7.5 Tf [(Statistics \(IWFOS\) in Brno, Czech Republic. The workshop was originally to be held on June 24-26, )] TJ ET
BT 34.016 96.063 Td /F1 7.5 Tf [(2020, but had to be postponed as a consequence of the COVID-19 pandemic. Initiated by the Working )] TJ ET
BT 34.016 86.906 Td /F1 7.5 Tf [(Group on Functional and Operatorial Statistics at the University of Toulouse in 2008, the IWFOS )] TJ ET
BT 34.016 77.748 Td /F1 7.5 Tf [(workshops provide a forum to discuss the latest trends and advances in functional statistics and related )] TJ ET
BT 34.016 68.591 Td /F1 7.5 Tf [(fields, and foster the exchange of ideas and international collaboration in the field.)] TJ ET
BT 34.016 59.433 Td /F1 7.5 Tf [(Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS)] TJ ET
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34.016 58.196 m 333.731 58.196 l S
BT 333.731 59.433 Td /F1 7.5 Tf [( Qingzhao Yu )] TJ ET
BT 34.016 50.276 Td /F1 7.5 Tf [(2022-03-14 Third-variable effect refers to the effect transmitted by third-variables that intervene in the )] TJ ET
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BT 34.016 553.938 Td /F1 7.5 Tf [(relationship between an exposure and a response variable. Differentiating between the indirect effect of )] TJ ET
BT 34.016 544.781 Td /F1 7.5 Tf [(individual factors from multiple third-variables is a constant problem for modern researchers. Statistical )] TJ ET
BT 34.016 535.623 Td /F1 7.5 Tf [(Methods for Mediation, Confounding and Moderation Analysis Using R and SAS introduces general )] TJ ET
BT 34.016 526.466 Td /F1 7.5 Tf [(definitions of third-variable effects that are adaptable to all different types of response \(categorical or )] TJ ET
BT 34.016 517.308 Td /F1 7.5 Tf [(continuous\), exposure, or third-variables. Using this method, multiple third- variables of different types )] TJ ET
BT 34.016 508.151 Td /F1 7.5 Tf [(can be considered simultaneously, and the indirect effect carried by individual third-variables can be )] TJ ET
BT 34.016 498.993 Td /F1 7.5 Tf [(separated from the total effect. Readers of all disciplines familiar with introductory statistics will find this a )] TJ ET
BT 34.016 489.836 Td /F1 7.5 Tf [(valuable resource for analysis. Key Features: Parametric and nonparametric method in third variable )] TJ ET
BT 34.016 480.678 Td /F1 7.5 Tf [(analysis Multivariate and Multiple third-variable effect analysis Multilevel mediation/confounding analysis )] TJ ET
BT 34.016 471.521 Td /F1 7.5 Tf [(Third-variable effect analysis with high-dimensional data Moderation/Interaction effect analysis within the )] TJ ET
BT 34.016 462.363 Td /F1 7.5 Tf [(third-variable analysis R packages and SAS macros to implement methods proposed in the book)] TJ ET
BT 34.016 453.206 Td /F1 7.5 Tf [(Analysis of Mixed Data)] TJ ET
0.15 w 0 J [ ] 0 d
34.016 451.968 m 110.291 451.968 l S
BT 110.291 453.206 Td /F1 7.5 Tf [( Alexander R. de Leon 2013-01-16 A comprehensive source on mixed data )] TJ ET
BT 34.016 444.048 Td /F1 7.5 Tf [(analysis, Analysis of Mixed Data: Methods & Applications summarizes the fundamental developments in )] TJ ET
BT 34.016 434.891 Td /F1 7.5 Tf [(the field. Case studies are used extensively throughout the book to illustrate interesting applications from )] TJ ET
BT 34.016 425.733 Td /F1 7.5 Tf [(economics, medicine and health, marketing, and genetics. Carefully edited for smooth readability and)] TJ ET
BT 34.016 416.576 Td /F1 7.5 Tf [(Principles and Theory for Data Mining and Machine Learning)] TJ ET
BT 235.766 416.576 Td /F1 7.5 Tf [( Bertrand Clarke 2009-07-21 Extensive )] TJ ET
BT 34.016 407.418 Td /F1 7.5 Tf [(treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging )] TJ ET
BT 34.016 398.261 Td /F1 7.5 Tf [(methods Range of topics drawn from Statistics, Computer Science, and Electrical Engineering)] TJ ET
BT 34.016 389.103 Td /F1 7.5 Tf [(High-Dimensional Covariance Matrix Estimation)] TJ ET
BT 192.798 389.103 Td /F1 7.5 Tf [( Aygul Zagidullina )] TJ ET
BT 34.016 379.946 Td /F1 7.5 Tf [(Object Oriented Data Analysis)] TJ ET
BT 134.471 379.946 Td /F1 7.5 Tf [( J. S. Marron 2021-11-18 Object Oriented Data Analysis is a framework )] TJ ET
BT 34.016 370.788 Td /F1 7.5 Tf [(that facilitates inter-disciplinary research through new terminology for discussing the often many possible )] TJ ET
BT 34.016 361.631 Td /F1 7.5 Tf [(approaches to the analysis of complex data. Such data are naturally arising in a wide variety of areas. )] TJ ET
BT 34.016 352.473 Td /F1 7.5 Tf [(This book aims to provide ways of thinking that enable the making of sensible choices. The main points )] TJ ET
BT 34.016 343.316 Td /F1 7.5 Tf [(are illustrated with many real data examples, based on the authors' personal experiences, which have )] TJ ET
BT 34.016 334.158 Td /F1 7.5 Tf [(motivated the invention of a wide array of analytic methods. While the mathematics go far beyond the )] TJ ET
BT 34.016 325.001 Td /F1 7.5 Tf [(usual in statistics \(including differential geometry and even topology\), the book is aimed at accessibility )] TJ ET
BT 34.016 315.843 Td /F1 7.5 Tf [(by graduate students. There is deliberate focus on ideas over mathematical formulas. J. S. Marron is the )] TJ ET
BT 34.016 306.686 Td /F1 7.5 Tf [(Amos Hawley Distinguished Professor of Statistics, Professor of Biostatistics, Adjunct Professor of )] TJ ET
BT 34.016 297.528 Td /F1 7.5 Tf [(Computer Science, Faculty Member of the Bioinformatics and Computational Biology Curriculum and )] TJ ET
BT 34.016 288.371 Td /F1 7.5 Tf [(Research Member of the Lineberger Cancer Center and the Computational Medicine Program, at the )] TJ ET
BT 34.016 279.213 Td /F1 7.5 Tf [(University of North Carolina, Chapel Hill. Ian L. Dryden is a Professor in the Department of Mathematics )] TJ ET
BT 34.016 270.056 Td /F1 7.5 Tf [(and Statistics at Florida International University in Miami, has served as Head of School of Mathematical )] TJ ET
BT 34.016 260.898 Td /F1 7.5 Tf [(Sciences at the University of Nottingham, and is joint author of the acclaimed book Statistical Shape )] TJ ET
BT 34.016 251.741 Td /F1 7.5 Tf [(Analysis.)] TJ ET
BT 34.016 242.583 Td /F1 7.5 Tf [(Big and Complex Data Analysis)] TJ ET
BT 139.053 242.583 Td /F1 7.5 Tf [( S. Ejaz Ahmed 2017-03-21 This volume conveys some of the surprises, )] TJ ET
BT 34.016 233.426 Td /F1 7.5 Tf [(puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-)] TJ ET
BT 34.016 224.268 Td /F1 7.5 Tf [(reviewed contributions showcase recent advances in variable selection, estimation and prediction )] TJ ET
BT 34.016 215.111 Td /F1 7.5 Tf [(strategies for a host of useful models, as well as essential new developments in the field. The continued )] TJ ET
BT 34.016 205.953 Td /F1 7.5 Tf [(and rapid advancement of modern technology now allows scientists to collect data of increasingly )] TJ ET
BT 34.016 196.796 Td /F1 7.5 Tf [(unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, )] TJ ET
BT 34.016 187.638 Td /F1 7.5 Tf [(high-resolution image data, high-frequency financial data, functional and longitudinal data, and network )] TJ ET
BT 34.016 178.481 Td /F1 7.5 Tf [(data. Simultaneous variable selection and estimation is one of the key statistical problems involved in )] TJ ET
BT 34.016 169.323 Td /F1 7.5 Tf [(analyzing such big and complex data. The purpose of this book is to stimulate research and foster )] TJ ET
BT 34.016 160.166 Td /F1 7.5 Tf [(interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals )] TJ ET
BT 34.016 151.008 Td /F1 7.5 Tf [(are to: 1\) highlight and expand the breadth of existing methods in big data and high-dimensional data )] TJ ET
BT 34.016 141.851 Td /F1 7.5 Tf [(analysis and their potential for the advancement of both the mathematical and statistical sciences; 2\) )] TJ ET
BT 34.016 132.693 Td /F1 7.5 Tf [(identify important directions for future research in the theory of regularization methods, in algorithmic )] TJ ET
BT 34.016 123.536 Td /F1 7.5 Tf [(development, and in methodologies for different application areas; and 3\) facilitate collaboration between )] TJ ET
BT 34.016 114.378 Td /F1 7.5 Tf [(theoretical and subject-specific researchers.)] TJ ET
BT 34.016 105.221 Td /F1 7.5 Tf [(Statistical Foundations of Data Science)] TJ ET
BT 164.493 105.221 Td /F1 7.5 Tf [( Jianqing Fan 2020-09-21 Statistical Foundations of Data Science )] TJ ET
BT 34.016 96.063 Td /F1 7.5 Tf [(gives a thorough introduction to commonly used statistical models, contemporary statistical machine )] TJ ET
BT 34.016 86.906 Td /F1 7.5 Tf [(learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims )] TJ ET
BT 34.016 77.748 Td /F1 7.5 Tf [(to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity )] TJ ET
BT 34.016 68.591 Td /F1 7.5 Tf [(and covariance learning, machine learning, and statistical inference. It includes ample exercises that )] TJ ET
BT 34.016 59.433 Td /F1 7.5 Tf [(involve both theoretical studies as well as empirical applications. The book begins with an introduction to )] TJ ET
BT 34.016 50.276 Td /F1 7.5 Tf [(the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear )] TJ ET
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BT 34.016 553.938 Td /F1 7.5 Tf [(regression and expands the techniques of model building via nonparametric regression and kernel tricks. )] TJ ET
BT 34.016 544.781 Td /F1 7.5 Tf [(It provides a comprehensive account on sparsity explorations and model selections for multiple )] TJ ET
BT 34.016 535.623 Td /F1 7.5 Tf [(regression, generalized linear models, quantile regression, robust regression, hazards regression, among )] TJ ET
BT 34.016 526.466 Td /F1 7.5 Tf [(others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book )] TJ ET
BT 34.016 517.308 Td /F1 7.5 Tf [(also provides a comprehensive account on high-dimensional covariance estimation, learning latent )] TJ ET
BT 34.016 508.151 Td /F1 7.5 Tf [(factors and hidden structures, as well as their applications to statistical estimation, inference, prediction )] TJ ET
BT 34.016 498.993 Td /F1 7.5 Tf [(and machine learning problems. It also introduces thoroughly statistical machine learning theory and )] TJ ET
BT 34.016 489.836 Td /F1 7.5 Tf [(methods for classification, clustering, and prediction. These include CART, random forests, boosting, )] TJ ET
BT 34.016 480.678 Td /F1 7.5 Tf [(support vector machines, clustering algorithms, sparse PCA, and deep learning.)] TJ ET
BT 34.016 471.521 Td /F1 7.5 Tf [(High-dimensional Statistics)] TJ ET
BT 124.038 471.521 Td /F1 7.5 Tf [( Johannes Lederer 2020 "An Introduction to Regularized Estimation in High )] TJ ET
BT 34.016 462.363 Td /F1 7.5 Tf [(Dimensions considers statistical theory, methods, and algorithms for large and complex data. The main )] TJ ET
BT 34.016 453.206 Td /F1 7.5 Tf [(focus is on regularized estimators, which are at the cusp of entering the statistical toolkits of almost all )] TJ ET
BT 34.016 444.048 Td /F1 7.5 Tf [(scientific disciplines. This book provides clear expositions, motivational introductions to each chapter, )] TJ ET
BT 34.016 434.891 Td /F1 7.5 Tf [(rigorous step-by-step proofs, and comprehensive exercise sets with fully worked out solutions. These )] TJ ET
BT 34.016 425.733 Td /F1 7.5 Tf [(features make this book ideal for graduate level courses. Moreover, the book also discusses cutting-edge )] TJ ET
BT 34.016 416.576 Td /F1 7.5 Tf [(topics, such as aspects of inference, robustness, and tuning parameters. The book also contains results )] TJ ET
BT 34.016 407.418 Td /F1 7.5 Tf [(and insights that are new altogether, including improvements of existing theories and novel connections )] TJ ET
BT 34.016 398.261 Td /F1 7.5 Tf [(among different methods, which are organized into special chapters for those wishing to advance their )] TJ ET
BT 34.016 389.103 Td /F1 7.5 Tf [(reading in the field."--)] TJ ET
BT 34.016 379.946 Td /F1 7.5 Tf [(Introduction to High-Dimensional Statistics)] TJ ET
BT 174.896 379.946 Td /F1 7.5 Tf [( Christophe Giraud 2021-08-26 Praise for the first edition: )] TJ ET
BT 34.016 370.788 Td /F1 7.5 Tf [("[This book] succeeds singularly at providing a structured introduction to this active field of research. ... it )] TJ ET
BT 34.016 361.631 Td /F1 7.5 Tf [(is arguably the most accessible overview yet published of the mathematical ideas and principles that one )] TJ ET
BT 34.016 352.473 Td /F1 7.5 Tf [(needs to master to enter the field of high-dimensional statistics. ... recommended to anyone interested in )] TJ ET
BT 34.016 343.316 Td /F1 7.5 Tf [(the main results of current research in high-dimensional statistics as well as anyone interested in )] TJ ET
BT 34.016 334.158 Td /F1 7.5 Tf [(acquiring the core mathematical skills to enter this area of research." —Journal of the American )] TJ ET
BT 34.016 325.001 Td /F1 7.5 Tf [(Statistical Association Introduction to High-Dimensional Statistics, Second Edition preserves the )] TJ ET
BT 34.016 315.843 Td /F1 7.5 Tf [(philosophy of the first edition: to be a concise guide for students and researchers discovering the area )] TJ ET
BT 34.016 306.686 Td /F1 7.5 Tf [(and interested in the mathematics involved. The main concepts and ideas are presented in simple )] TJ ET
BT 34.016 297.528 Td /F1 7.5 Tf [(settings, avoiding thereby unessential technicalities. High-dimensional statistics is a fast-evolving field, )] TJ ET
BT 34.016 288.371 Td /F1 7.5 Tf [(and much progress has been made on a large variety of topics, providing new insights and methods. )] TJ ET
BT 34.016 279.213 Td /F1 7.5 Tf [(Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this new )] TJ ET
BT 34.016 270.056 Td /F1 7.5 Tf [(edition: Offers revised chapters from the previous edition, with the inclusion of many additional materials )] TJ ET
BT 34.016 260.898 Td /F1 7.5 Tf [(on some important topics, including compress sensing, estimation with convex constraints, the slope )] TJ ET
BT 34.016 251.741 Td /F1 7.5 Tf [(estimator, simultaneously low-rank and row-sparse linear regression, or aggregation of a continuous set )] TJ ET
BT 34.016 242.583 Td /F1 7.5 Tf [(of estimators. Introduces three new chapters on iterative algorithms, clustering, and minimax lower )] TJ ET
BT 34.016 233.426 Td /F1 7.5 Tf [(bounds. Provides enhanced appendices, minimax lower-bounds mainly with the addition of the Davis-)] TJ ET
BT 34.016 224.268 Td /F1 7.5 Tf [(Kahan perturbation bound and of two simple versions of the Hanson-Wright concentration inequality. )] TJ ET
BT 34.016 215.111 Td /F1 7.5 Tf [(Covers cutting-edge statistical methods including model selection, sparsity and the Lasso, iterative hard )] TJ ET
BT 34.016 205.953 Td /F1 7.5 Tf [(thresholding, aggregation, support vector machines, and learning theory. Provides detailed exercises at )] TJ ET
BT 34.016 196.796 Td /F1 7.5 Tf [(the end of every chapter with collaborative solutions on a wiki site. Illustrates concepts with simple but )] TJ ET
BT 34.016 187.638 Td /F1 7.5 Tf [(clear practical examples.)] TJ ET
BT 34.016 178.481 Td /F1 7.5 Tf [(Principles and Methods for Data Science)] TJ ET
BT 169.488 178.481 Td /F1 7.5 Tf [( 2020-05-28 Principles and Methods for Data Science, Volume )] TJ ET
BT 34.016 169.323 Td /F1 7.5 Tf [(43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume )] TJ ET
BT 34.016 160.166 Td /F1 7.5 Tf [(presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and )] TJ ET
BT 34.016 151.008 Td /F1 7.5 Tf [(mining of microbial community dynamics, Support Vector Machines, a robust prediction method with )] TJ ET
BT 34.016 141.851 Td /F1 7.5 Tf [(applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional )] TJ ET
BT 34.016 132.693 Td /F1 7.5 Tf [(statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis )] TJ ET
BT 34.016 123.536 Td /F1 7.5 Tf [(of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, )] TJ ET
BT 34.016 114.378 Td /F1 7.5 Tf [(Markov Chain Monte Carlo Methods: Theory and Practice, and more. Provides the authority and )] TJ ET
BT 34.016 105.221 Td /F1 7.5 Tf [(expertise of leading contributors from an international board of authors Presents the latest release in the )] TJ ET
BT 34.016 96.063 Td /F1 7.5 Tf [(Handbook of Statistics series Updated release includes the latest information on Principles and Methods )] TJ ET
BT 34.016 86.906 Td /F1 7.5 Tf [(for Data Science)] TJ ET
BT 34.016 77.748 Td /F1 7.5 Tf [(Analysis of Multivariate and High-Dimensional Data)] TJ ET
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34.016 76.511 m 204.483 76.511 l S
BT 204.483 77.748 Td /F1 7.5 Tf [( Inge Koch 2013-12-02 This modern approach )] TJ ET
BT 34.016 68.591 Td /F1 7.5 Tf [(integrates classical and contemporary methods, fusing theory and practice and bridging the gap to )] TJ ET
BT 34.016 59.433 Td /F1 7.5 Tf [(statistical learning.)] TJ ET
BT 34.016 50.276 Td /F1 7.5 Tf [(High-Dimensional Statistics)] TJ ET
BT 125.283 50.276 Td /F1 7.5 Tf [( Martin J. Wainwright 2019-02-21 A coherent introductory text from a )] TJ ET
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BT 34.016 553.938 Td /F1 7.5 Tf [(groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.)] TJ ET
BT 34.016 544.781 Td /F1 7.5 Tf [(Modern Directional Statistics)] TJ ET
BT 129.041 544.781 Td /F1 7.5 Tf [( Christophe Ley 2017-08-03 Modern Directional Statistics collects important )] TJ ET
BT 34.016 535.623 Td /F1 7.5 Tf [(advances in methodology and theory for directional statistics over the last two decades. It provides a )] TJ ET
BT 34.016 526.466 Td /F1 7.5 Tf [(detailed overview and analysis of recent results that can help both researchers and practitioners. )] TJ ET
BT 34.016 517.308 Td /F1 7.5 Tf [(Knowledge of multivariate statistics eases the reading but is not mandatory. The field of directional )] TJ ET
BT 34.016 508.151 Td /F1 7.5 Tf [(statistics has received a lot of attention over the past two decades, due to new demands from domains )] TJ ET
BT 34.016 498.993 Td /F1 7.5 Tf [(such as life sciences or machine learning, to the availability of massive data sets requiring adapted )] TJ ET
BT 34.016 489.836 Td /F1 7.5 Tf [(statistical techniques, and to technological advances. This book covers important progresses in )] TJ ET
BT 34.016 480.678 Td /F1 7.5 Tf [(distribution theory,high-dimensional statistics, kernel density estimation, efficient inference on directional )] TJ ET
BT 34.016 471.521 Td /F1 7.5 Tf [(supports, and computational and graphical methods. Christophe Ley is professor of mathematical )] TJ ET
BT 34.016 462.363 Td /F1 7.5 Tf [(statistics at Ghent University. His research interests include semi-parametrically efficient inference, )] TJ ET
BT 34.016 453.206 Td /F1 7.5 Tf [(flexible modeling, directional statistics and the study of asymptotic approximations via Stein’s Method. )] TJ ET
BT 34.016 444.048 Td /F1 7.5 Tf [(His achievements include the Marie-Jeanne Laurent-Duhamel prize of the Société Française de )] TJ ET
BT 34.016 434.891 Td /F1 7.5 Tf [(Statistique and an elected membership at the International Statistical Institute. He is associate editor for )] TJ ET
BT 34.016 425.733 Td /F1 7.5 Tf [(the journals Computational Statistics & Data Analysis and Econometrics and Statistics. Thomas )] TJ ET
BT 34.016 416.576 Td /F1 7.5 Tf [(Verdebout is professor of mathematical statistics at Université libre de Bruxelles \(ULB\). His main )] TJ ET
BT 34.016 407.418 Td /F1 7.5 Tf [(research interests are semi-parametric statistics, high- dimensional statistics, directional statistics and )] TJ ET
BT 34.016 398.261 Td /F1 7.5 Tf [(rank-based procedures. He has won an annual prize of the Belgian Academy of Sciences and is an )] TJ ET
BT 34.016 389.103 Td /F1 7.5 Tf [(elected member of the International Statistical Institute. He is associate editor for the journals Statistics )] TJ ET
BT 34.016 379.946 Td /F1 7.5 Tf [(and Probability Letters and Journal of Multivariate Analysis.)] TJ ET
BT 34.016 370.788 Td /F1 7.5 Tf [(Contributions in infinite-dimensional statistics and related topics)] TJ ET
BT 244.511 370.788 Td /F1 7.5 Tf [( Enea G. Bongiorno 2014-05-21 The )] TJ ET
BT 34.016 361.631 Td /F1 7.5 Tf [(interest towards Functional and Operatorial Statistics, and, more in general, towards infinite-dimensional )] TJ ET
BT 34.016 352.473 Td /F1 7.5 Tf [(statistics has dramatically increased in the statistical community and in many other applied scientific )] TJ ET
BT 34.016 343.316 Td /F1 7.5 Tf [(areas where people faces functional data. This volume collects the works selected and presented at the )] TJ ET
BT 34.016 334.158 Td /F1 7.5 Tf [(Third Edition of the International Workshop on Functional and Operatorial Statistics held in Stresa, Italy, )] TJ ET
BT 34.016 325.001 Td /F1 7.5 Tf [(from the 19th to the 21st of June 2014 \(IWFOS’2014\). The meeting represents an opportunity of bringing )] TJ ET
BT 34.016 315.843 Td /F1 7.5 Tf [(together leading researchers active on these topics both for what concerns theoretical aspects and a )] TJ ET
BT 34.016 306.686 Td /F1 7.5 Tf [(wide range of applications in various fields. To promote collaborations with other important strictly related )] TJ ET
BT 34.016 297.528 Td /F1 7.5 Tf [(areas of infinite-dimensional Statistics, such as High Dimensional Statistics and Model Selection )] TJ ET
BT 34.016 288.371 Td /F1 7.5 Tf [(Procedures, this book hosts works in the latter research subjects too.)] TJ ET
BT 34.016 279.213 Td /F1 7.5 Tf [(Inverse Problems and High-Dimensional Estimation)] TJ ET
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BT 205.316 279.213 Td /F1 7.5 Tf [( Pierre Alquier 2011-06-07 The “Stats in the Château” )] TJ ET
BT 34.016 270.056 Td /F1 7.5 Tf [(summer school was held at the CRC château on the campus of HEC Paris, Jouy-en-Josas, France, from )] TJ ET
BT 34.016 260.898 Td /F1 7.5 Tf [(August 31 to September 4, 2009. This event was organized jointly by faculty members of three French )] TJ ET
BT 34.016 251.741 Td /F1 7.5 Tf [(academic institutions ? ENSAE ParisTech, the Ecole Polytechnique ParisTech, and HEC Paris ? which )] TJ ET
BT 34.016 242.583 Td /F1 7.5 Tf [(cooperate through a scientific foundation devoted to the decision sciences. The scientific content of the )] TJ ET
BT 34.016 233.426 Td /F1 7.5 Tf [(summer school was conveyed in two courses, one by Laurent Cavalier \(Université Aix-Marseille I\) on "Ill-)] TJ ET
BT 34.016 224.268 Td /F1 7.5 Tf [(posed Inverse Problems", and one by Victor Chernozhukov \(Massachusetts Institute of Technology\) on )] TJ ET
BT 34.016 215.111 Td /F1 7.5 Tf [("High-dimensional Estimation with Applications to Economics". Ten invited researchers also presented )] TJ ET
BT 34.016 205.953 Td /F1 7.5 Tf [(either reviews of the state of the art in the field or of applications, or original research contributions. This )] TJ ET
BT 34.016 196.796 Td /F1 7.5 Tf [(volume contains the lecture notes of the two courses. Original research articles and a survey )] TJ ET
BT 34.016 187.638 Td /F1 7.5 Tf [(complement these lecture notes. Applications to economics are discussed in various contributions.)] TJ ET
BT 34.016 178.481 Td /F1 7.5 Tf [(Mathematical Foundations of Infinite-Dimensional Statistical Models)] TJ ET
BT 34.016 169.323 Td /F1 7.5 Tf [(High-Dimensional Covariance Estimation)] TJ ET
BT 170.298 169.323 Td /F1 7.5 Tf [( Mohsen Pourahmadi 2013-06-24 "Focusing on methodology )] TJ ET
BT 34.016 160.166 Td /F1 7.5 Tf [(and computation more than on theorems and proofs, this book provides computationally feasible and )] TJ ET
BT 34.016 151.008 Td /F1 7.5 Tf [(statistically efficient methods for estimating sparse and large covariance matrices of high-dimensional )] TJ ET
BT 34.016 141.851 Td /F1 7.5 Tf [(data. Extensive in breadth and scope, it features ample applications to a number of applied areas, )] TJ ET
BT 34.016 132.693 Td /F1 7.5 Tf [(including business and economics, computer science, engineering, and financial mathematics; )] TJ ET
BT 34.016 123.536 Td /F1 7.5 Tf [(recognizes the important and significant contributions of longitudinal and spatial data; and includes )] TJ ET
BT 34.016 114.378 Td /F1 7.5 Tf [(various computer codes in R throughout the text and on an author-maintained web site"--)] TJ ET
BT 34.016 105.221 Td /F1 7.5 Tf [(Modern Multivariate Statistical Techniques)] TJ ET
BT 174.896 105.221 Td /F1 7.5 Tf [( Alan J. Izenman 2009-03-02 This is the first book on )] TJ ET
BT 34.016 96.063 Td /F1 7.5 Tf [(multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. )] TJ ET
BT 34.016 86.906 Td /F1 7.5 Tf [(Material such as database management systems is included that has never appeared in statistics books )] TJ ET
BT 34.016 77.748 Td /F1 7.5 Tf [(before.)] TJ ET
BT 34.016 68.591 Td /F1 7.5 Tf [(Foundations of Data Science)] TJ ET
BT 130.316 68.591 Td /F1 7.5 Tf [( Avrim Blum 2020-01-31 Covers mathematical and algorithmic foundations )] TJ ET
BT 34.016 59.433 Td /F1 7.5 Tf [(of data science: machine learning, high-dimensional geometry, and analysis of large networks.)] TJ ET
BT 34.016 50.276 Td /F1 7.5 Tf [(Multiple Testing Procedures with Applications to Genomics)] TJ ET
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BT 229.083 50.276 Td /F1 7.5 Tf [( Sandrine Dudoit 2007-12-18 This book )] TJ ET
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BT 34.016 553.938 Td /F1 7.5 Tf [(establishes the theoretical foundations of a general methodology for multiple hypothesis testing and )] TJ ET
BT 34.016 544.781 Td /F1 7.5 Tf [(discusses its software implementation in R and SAS. These are applied to a range of problems in )] TJ ET
BT 34.016 535.623 Td /F1 7.5 Tf [(biomedical and genomic research, including identification of differentially expressed and co-expressed )] TJ ET
BT 34.016 526.466 Td /F1 7.5 Tf [(genes in high-throughput gene expression experiments; tests of association between gene expression )] TJ ET
BT 34.016 517.308 Td /F1 7.5 Tf [(measures and biological annotation metadata; sequence analysis; and genetic mapping of complex traits )] TJ ET
BT 34.016 508.151 Td /F1 7.5 Tf [(using single nucleotide polymorphisms. The procedures are based on a test statistics joint null )] TJ ET
BT 34.016 498.993 Td /F1 7.5 Tf [(distribution and provide Type I error control in testing problems involving general data generating )] TJ ET
BT 34.016 489.836 Td /F1 7.5 Tf [(distributions, null hypotheses, and test statistics.)] TJ ET
BT 34.016 480.678 Td /F1 7.5 Tf [(High-Dimensional Probability)] TJ ET
BT 130.286 480.678 Td /F1 7.5 Tf [( Roman Vershynin 2018-09-27 An integrated package of powerful )] TJ ET
BT 34.016 471.521 Td /F1 7.5 Tf [(probabilistic tools and key applications in modern mathematical data science.)] TJ ET
BT 34.016 462.363 Td /F1 7.5 Tf [(Geometric Structure of High-Dimensional Data and Dimensionality Reduction)] TJ ET
BT 289.931 462.363 Td /F1 7.5 Tf [( Jianzhong Wang 2012-04-)] TJ ET
BT 34.016 453.206 Td /F1 7.5 Tf [(28 "Geometric Structure of High-Dimensional Data and Dimensionality Reduction" adopts data geometry )] TJ ET
BT 34.016 444.048 Td /F1 7.5 Tf [(as a framework to address various methods of dimensionality reduction. In addition to the introduction to )] TJ ET
BT 34.016 434.891 Td /F1 7.5 Tf [(well-known linear methods, the book moreover stresses the recently developed nonlinear methods and )] TJ ET
BT 34.016 425.733 Td /F1 7.5 Tf [(introduces the applications of dimensionality reduction in many areas, such as face recognition, image )] TJ ET
BT 34.016 416.576 Td /F1 7.5 Tf [(segmentation, data classification, data visualization, and hyperspectral imagery data analysis. Numerous )] TJ ET
BT 34.016 407.418 Td /F1 7.5 Tf [(tables and graphs are included to illustrate the ideas, effects, and shortcomings of the methods. MATLAB )] TJ ET
BT 34.016 398.261 Td /F1 7.5 Tf [(code of all dimensionality reduction algorithms is provided to aid the readers with the implementations on )] TJ ET
BT 34.016 389.103 Td /F1 7.5 Tf [(computers. The book will be useful for mathematicians, statisticians, computer scientists, and data )] TJ ET
BT 34.016 379.946 Td /F1 7.5 Tf [(analysts. It is also a valuable handbook for other practitioners who have a basic background in )] TJ ET
BT 34.016 370.788 Td /F1 7.5 Tf [(mathematics, statistics and/or computer algorithms, like internet search engine designers, physicists, )] TJ ET
BT 34.016 361.631 Td /F1 7.5 Tf [(geologists, electronic engineers, and economists. Jianzhong Wang is a Professor of Mathematics at Sam )] TJ ET
BT 34.016 352.473 Td /F1 7.5 Tf [(Houston State University, U.S.A.)] TJ ET
BT 34.016 343.316 Td /F1 7.5 Tf [(Large Sample Covariance Matrices and High-Dimensional Data Analysis)] TJ ET
BT 274.923 343.316 Td /F1 7.5 Tf [( Jianfeng Yao 2015-03-26 High-)] TJ ET
BT 34.016 334.158 Td /F1 7.5 Tf [(dimensional data appear in many fields, and their analysis has become increasingly important in modern )] TJ ET
BT 34.016 325.001 Td /F1 7.5 Tf [(statistics. However, it has long been observed that several well-known methods in multivariate analysis )] TJ ET
BT 34.016 315.843 Td /F1 7.5 Tf [(become inefficient, or even misleading, when the data dimension p is larger than, say, several tens. A )] TJ ET
BT 34.016 306.686 Td /F1 7.5 Tf [(seminal example is the well-known inefficiency of Hotelling's T2-test in such cases. This example shows )] TJ ET
BT 34.016 297.528 Td /F1 7.5 Tf [(that classical large sample limits may no longer hold for high-dimensional data; statisticians must seek )] TJ ET
BT 34.016 288.371 Td /F1 7.5 Tf [(new limiting theorems in these instances. Thus, the theory of random matrices \(RMT\) serves as a much-)] TJ ET
BT 34.016 279.213 Td /F1 7.5 Tf [(needed and welcome alternative framework. Based on the authors' own research, this book provides a )] TJ ET
BT 34.016 270.056 Td /F1 7.5 Tf [(first-hand introduction to new high-dimensional statistical methods derived from RMT. The book begins )] TJ ET
BT 34.016 260.898 Td /F1 7.5 Tf [(with a detailed introduction to useful tools from RMT, and then presents a series of high-dimensional )] TJ ET
BT 34.016 251.741 Td /F1 7.5 Tf [(problems with solutions provided by RMT methods.)] TJ ET
BT 34.016 242.583 Td /F1 7.5 Tf [(High-Dimensional Data Analysis with Low-Dimensional Models)] TJ ET
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BT 242.396 242.583 Td /F1 7.5 Tf [( John Wright 2021-12-31 Connects )] TJ ET
BT 34.016 233.426 Td /F1 7.5 Tf [(fundamental mathematical theory with real-world problems, through efficient and scalable optimization )] TJ ET
BT 34.016 224.268 Td /F1 7.5 Tf [(algorithms.)] TJ ET
BT 36.266 195.872 Td /F1 8.0 Tf [(statistics-for-high-dimensional-data-methods-)] TJ ET
BT 36.266 186.104 Td /F1 8.0 Tf [(theory-and-applications)] TJ ET
BT 213.424 196.080 Td /F1 8.0 Tf [(Downloaded from )] TJ ET
BT 278.336 195.872 Td /F1 8.0 Tf [(siamguru.com)] TJ ET
BT 328.568 196.080 Td /F1 8.0 Tf [( on September )] TJ ET
BT 319.664 186.312 Td /F1 8.0 Tf [(24, 2022 by guest)] TJ ET
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