LEADER 03731nam 2200577 450 001 9910829984503321 005 20211009102710.0 010 $a1-119-41741-4 010 $a1-119-41740-6 010 $a1-119-41739-2 035 $a(CKB)4100000011788467 035 $a(MiAaPQ)EBC6508332 035 $a(Au-PeEL)EBL6508332 035 $a(OCoLC-P)1193558110 035 $a(CaSebORM)9781119417385 035 $a(OCoLC)1193558110 035 $a(EXLCZ)994100000011788467 100 $a20211009d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical learning for big dependent data /$fDaniel Pen?a, Ruey S. Tsay 205 $aFirst edition. 210 1$aHoboken, New Jersey :$cWiley,$d[2021] 210 4$dİ2021 215 $a1 online resource (563 pages) 225 1 $aWiley series in probability and statistics 311 18$a1-119-41738-4 327 $aIntroduction 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. 330 $a"This book presents methods useful for analyzing and understanding large data sets that are dynamically dependent. The book will begin with examples of multivariate dependent data and tools for presenting descriptive statistics of such data. It then introduces some useful statistical methods for univariate time series analysis emphasizing on statistical procedures for modeling and forecasting. Both linear and nonlinear models are discussed. Special attention is given to analysis of high-frequency dependent data. The second part of the book considers joint dependency, both contemporaneous and dynamical dependence, among multiple series of dependent data. Special attention will be given to graphical methods for large data, to handling heterogeneity in time series (such as outliers, missing values, and changes in the covariance matrices), and to time-varying parameters for multivariate time series. The third part of the book is devoted to analysis of high-dimensional dependent data. The focus is on topics that are useful when the number of time series is large. The selected topics include clustering time series, high-dimensional linear regression for dependent data and its applications, and reducing the dimension with dynamic principal components and factor models. Throughout the book, advantages and disadvantages of the methods discussed are given and real examples are used in demonstration. The book will be of interest to graduate students, researchers, and practitioners in business, economics, engineering, and science who are interested in statistical methods for analyzing big dependent data and forecasting"--$cProvided by publisher. 410 0$aWiley series in probability and statistics. 606 $aBig data$xMathematics 606 $aTime-series analysis 606 $aData mining$xStatistical methods 606 $aForecasting$xStatistical methods 615 0$aBig data$xMathematics. 615 0$aTime-series analysis. 615 0$aData mining$xStatistical methods. 615 0$aForecasting$xStatistical methods. 676 $a005.7 700 $aPen?a$b Daniel$f1948-$0614022 702 $aTsay$b Ruey S.$f1951- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910829984503321 996 $aStatistical learning for big dependent data$94014473 997 $aUNINA