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001			21709313
005			20250930144153.0
006			m |o d |
007			cr |||||||||||
008			200815s2021 nju ob 001 0 eng
010			_a2020026631
020			_a9781119417385 _qpaperback _cRM695.04
040			_aDLC _beng _cDLC _erda _dUKM
042			_apcc
082	0	0	_a005.7 _223
090	0	0	_aQA76.9 _b.B45 3
100	1		_aPenÌa, Daniel, _d1948- _eauthor.
245	1	0	_aStatistical learning for big dependent data / _cDaniel PenÌa, Ruey S. Tsay.
250			_aFirst edition.
263			_a2101
264		1	_aHoboken, NJ : _bWiley, _c2021.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	0		_aWiley series in probability and statistics
504			_aIncludes bibliographical references and index.
505	0		_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.
520			_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.
588			_aDescription based on print version record and CIP data provided by publisher; resource not viewed.
650		0	_aBig data _xMathematics.
650		0	_aTime-series analysis. _961130
650		0	_aData mining _xStatistical methods.
650		0	_aForecasting _xStatistical methods.
700	1		_aTsay, Ruey S., _d1951- _eauthor.
776	0	8	_iPrint version: _aPenÌa, Daniel, 1948- _tStatistical learning for big dependent data _bFirst edition. _dHoboken, NJ : Wiley, 2021. _z9781119417385 _w(DLC) 2020026630
907			_a.b16964925 _b2022-12-22 _c2022-12-15
942			_c01 _n0 _kQA76.9 .B45 3
991			_aFakulti Teknologi Sains Maklumat
998			_al _b2022-12-15 _cm _da _feng _gnju _y0 _z.b16964925
999			_c663389 _d663389