000			02103 a2200241 4500
003			OSt
005			20210707103756.0
008			210204b xxu||||| |||| 00| 0 eng d
040			_cIIT Kanpur
041			_aeng
082			_a519.55 _bB783t
100			_aBrockwell, Peter J.
245			_aTime series [Perpetual] _btheory and methods _cPeter J. Brockwell and Richard A. Davis
260			_bSpringer _c1987 _aNew York
300			_axiv, 532p
440			_aSpringer series in statistics
520			_aWe have attempted in this book to give a systematic account of linear time series models and their application to the modelling and prediction of data collected sequentially in time. The aim is to provide specific techniques for handling data and at the same time to provide a thorough understanding of the mathematical basis for the techniques. Both time and frequency domain methods are discussed but the book is written in such a way that either approach could be emphasized. The book is intended to be a text for graduate students in statistics, mathematics, engineering, and the natural or social sciences. It has been used both at the M. S. level, emphasizing the more practical aspects of modelling, and at the Ph. D. level, where the detailed mathematical derivations of the deeper results can be included. Distinctive features of the book are the extensive use of elementary Hilbert space methods and recursive prediction techniques based on innovations, use of the exact Gaussian likelihood and AIC for inference, a thorough treatment of the asymptotic behavior of the maximum likelihood estimators of the coefficients of univariate ARMA models, extensive illustrations of the tech­ niques by means of numerical examples, and a large number of problems for the reader. The companion diskette contains programs written for the IBM PC, which can be used to apply the methods described in the text.
650			_aTime-series analysis
650			_aPrediction theory
700			_aDavis, Richard A.
856			_uhttps://link.springer.com/book/10.1007/978-1-4899-0004-3
942			_cEBK
999			_c563590 _d563590