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Statistical learning for big dependent data / Daniel Pe�na, Ruey S. Tsay
(Wiley series in probability and statistics)

データ種別	電子ブック
版	First edition.
出版者	(Hoboken, NJ : John Wiley & Sons, Inc)
出版年	2021
大きさ	1 online resource
著者標目	*Pe�na, Daniel 1948- author Tsay, Ruey S. 1951- author

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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0005232	Wiley Online Library: Complete oBooks				9781119417408

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一般注記	Includes bibliographical references and index 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 "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 Description based on online resource; title from digital title page (viewed on July 08, 2021) John Wiley and Sons Wiley Online Library: Complete oBooks HTTP:URL=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119417408
件　名	LCSH:Big data -- Mathematics 全ての件名で検索 LCSH:Time-series analysis LCSH:Data mining -- Statistical methods 全ての件名で検索 LCSH:Forecasting -- Statistical methods 全ての件名で検索 CSHF:Donn�ees volumineuses -- Math�ematiques 全ての件名で検索 CSHF:S�erie chronologique CSHF:Pr�evision -- M�ethodes statistiques 全ての件名で検索 FREE:Data mining -- Statistical methods 全ての件名で検索 FREE:Forecasting -- Statistical methods 全ての件名で検索 FREE:Time-series analysis
分　類	LCC:QA76.9.B45 DC23:005.7
書誌ID	EB00004521
ISBN	9781119417408