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Gaussian and Non-Gaussian Linear Time Series and Random Fields

Download or Read eBook Gaussian and Non-Gaussian Linear Time Series and Random Fields PDF written by Murray Rosenblatt and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 252 pages. Available in PDF, EPUB and Kindle.
Gaussian and Non-Gaussian Linear Time Series and Random Fields
Author :
Publisher : Springer Science & Business Media
Total Pages : 252
Release :
ISBN-10 : 9781461212621
ISBN-13 : 1461212626
Rating : 4/5 (21 Downloads)

Book Synopsis Gaussian and Non-Gaussian Linear Time Series and Random Fields by : Murray Rosenblatt

Book excerpt: The principal focus here is on autoregressive moving average models and analogous random fields, with probabilistic and statistical questions also being discussed. The book contrasts Gaussian models with noncausal or noninvertible (nonminimum phase) non-Gaussian models and deals with problems of prediction and estimation. New results for nonminimum phase non-Gaussian processes are exposited and open questions are noted. Intended as a text for gradutes in statistics, mathematics, engineering, the natural sciences and economics, the only recommendation is an initial background in probability theory and statistics. Notes on background, history and open problems are given at the end of the book.


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