Statistical Learning for Big Dependent Data - (Wiley Probability and Statistics) by Daniel Peña & Ruey S Tsay (Hardcover)
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<p/><br></br><p><b> About the Book </b></p></br></br>"This book presents methods useful for analyzing and understanding large data sets that are dynamically dependent. The book will begin with examples of multivariate dependent data and tools for presenting descriptive statistics of such data. It then introduces some useful statistical methods for univariate time series analysis emphasizing on statistical procedures for modeling and forecasting. Both linear and nonlinear models are discussed. Special attention is given to analysis of high-frequency dependent data. The second part of the book considers joint dependency, both contemporaneous and dynamical dependence, among multiple series of dependent data. Special attention will be given to graphical methods for large data, to handling heterogeneity in time series (such as outliers, missing values, and changes in the covariance matrices), and to time-varying parameters for multivariate time series. The third part of the book is devoted to analysis of high-dimensional dependent data. The focus is on topics that are useful when the number of time series is large. The selected topics include clustering time series, high-dimensional linear regression for dependent data and its applications, and reducing the dimension with dynamic principal components and factor models. Throughout the book, advantages and disadvantages of the methods discussed are given and real examples are used in demonstration. The book will be of interest to graduate students, researchers, and practitioners in business, economics, engineering, and science who are interested in statistical methods for analyzing big dependent data and forecasting"--<p/><br></br><p><b> Book Synopsis </b></p></br></br><p><b>Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource</b></p> <p><i>Statistical Learning with Big Dependent Data</i> delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented.</p> <p>Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications.</p> <p><i>Analysis of Big Dependent Data </i>includes a wide variety of topics for modeling and understanding big dependent data, like: </p> <ul> <li>New ways to plot large sets of time series</li> <li>An automatic procedure to build univariate ARMA models for individual components of a large data set</li> <li>Powerful outlier detection procedures for large sets of related time series</li> <li>New methods for finding the number of clusters of time series and discrimination methods, including vector support machines, for time series</li> <li>Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models</li> <li>Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series</li> <li>Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting.</li> <li>Introduction of modern procedures for modeling and forecasting spatio-temporal data </li> </ul> <p>Perfect for PhD students and researchers in business, economics, engineering, and science: <i>Statistical Learning with Big Dependent Data </i>also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.</p><p/><br></br><p><b> About the Author </b></p></br></br><p><b>Daniel Peña, PhD, </b> is Professor of Statistics at Universidad Carlos III de Madrid, Spain. He received his PhD from Universidad Politecnica de Madrid in 1976 and has taught at the Universities of Wisconsin-Madison, Chicago and Carlos III de Madrid, where he was Rector from 2007 to 2015. </p><p><b>Ruey S. Tsay, PhD, </b> is the H.G.B Alexander Professor of Econometrics & Statistics at the Booth School of Business, University of Chicago, United States. He received his PhD in 1982 from the University of Wisconsin-Madison. His research focuses on areas of business and economic forecasting, financial econometrics, risk management, and analysis of big dependent data.</p>
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