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RT Book, Whole SR Electronic DC OPAC T1 Statistical learning for big dependent data / Daniel Pe�na, Ruey S. Tsay T2 Wiley series in probability and statistics A1 Pe�na, Daniel 1948- A1 Tsay, Ruey S. 1951- YR 2021 FD 2021 SP 1 online resource K1 Big data -- Mathematics K1 Time-series analysis K1 Data mining -- Statistical methods K1 Forecasting -- Statistical methods K1 Donn�ees volumineuses -- Math�ematiques K1 S�erie chronologique K1 Pr�evision -- M�ethodes statistiques K1 Data mining -- Statistical methods K1 Forecasting -- Statistical methods K1 Time-series analysis ED First edition. PB John Wiley & Sons, Inc PP Hoboken, NJ SN 9781119417408 SN 1119417406 SN 9781119417392 SN 1119417392 SN 9781119417415 SN 1119417414 LA English (英語) CL LCC:QA76.9.B45 CL DC23:005.7 NO Includes bibliographical references and index NO Introduction to big dependent data -- Linear univariate time series -- Analysis of multivariate time series -- Handling heterogeneity in many time series -- Clustering and classification of time series -- Dynamic factor models -- Forecasting with big dependent data -- Machine learning of big dependent data -- Spatio-temporal dependent data NO "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"-- Provided by publisher NO Description based on online resource; title from digital title page (viewed on July 08, 2021) NO John Wiley and Sons Wiley Online Library: Complete oBooks NO HTTP:URL=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119417408 NO 書誌ID=EB00004521; LK [E Book]https://onlinelibrary.wiley.com/doi/book/10.1002/9781119417408 OL 30