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Gaussian and Non-Gaussian Linear Time Series and Random Fields (Springer Series in Statistics) - Hardcover
Synopsis
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.
"synopsis" may belong to another edition of this title.
About the Author
Murray Rosenblatt is Professor of Mathematics at the University of California, San Diego. He was a Guggenheim Fellow in 1965 and 1972 and is a member of the National Academy of Sciences, U.S.A. He is the author of "Random Processes" (1962), "Markov Processes: Structure and Asymptotic Behavior" (1971), "Stationary Sequences and Random Fields" (1985), and "Stochastic Curve Estimation" (1991).
Review
From the reviews:
SHORT BOOK REVIEWS
"...will make this book useful as a reference source to the more theoretical among time series specialists."
ZENTRALBLATT MATH
"This publication can be recommended to readers familiar with the basic concepts of time series who are interested in estimation problems in nonminimum phase processes."
"About this title" may belong to another edition of this title.
- PublisherSpringer
- Publication date1999
- ISBN 10 038798917X
- ISBN 13 9780387989174
- BindingHardcover
- LanguageEnglish
- Number of pages260
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Gaussian and Non-Gaussian Linear Time Series and Random Fields (Springer Series in Statistics)
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Condition: Fine. *FREE DOMESTIC SHIPPING until Monday, April 7* 260 pp., Hardcover, spine faded else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Seller Inventory # ZB1266655
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Gaussian and Non-Gaussian Linear Time Series and Random Fields.
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New York, NY, U.S.A. Springer Verlag, 1999
ISBN 10: 038798917X
ISBN 13: 9780387989174
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Gebunden. Condition: Sehr gut. Gebraucht - Sehr gut Zustand: Sehr gut, XIII, 246 pp. About this book 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. Written for researchers, graduate students. Seller Inventory # 18330
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Gaussian and Non-Gaussian Linear Time Series and Random Fields (Springer Series in Statistics)
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Gaussian and Non-Gaussian Linear Time Series and Random Fields (Springer Series in Statistics)
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Gaussian and Non-Gaussian Linear Time Series and Random Fields (Springer Series in Statistics)
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Springer New York Dez 1999, 1999
ISBN 10: 038798917X
ISBN 13: 9780387989174
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Hardcover
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book is concerned with linear time series and random fields in both the Gaussian and especially the non-Gaussian context. The principal focus is on autoregressive moving average models and analogous random fields. Probabilistic and statistical questions are both discussed. The Gaussian models are contrasted with noncausal or noninvertible (nonminimum phase) non-Gaussian models which can have a much richer structure than Gaussian models. The book deals with problems of prediction (which can have a nonlinear character) and estimation. New results for nonminimum phase non-Gaussian processes are exposited and open questions are noted. The book is intended as a text for graduate students in statistics, mathematics, engineering, the natural sciences and economics. An initial background in probability theory and statistics is suggested. Notes on background, history and open problems are given at the end of the book. 268 pp. Englisch. Seller Inventory # 9780387989174
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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-Gau. Seller Inventory # 5913570
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Much of this book is concerned with autoregressive and moving av erage linear stationary sequences and random fields. These models are part of the classical literature in time series analysis, particularly in the Gaussian case. There is a large literature on probabilistic and statistical aspects of these models-to a great extent in the Gaussian context. In the Gaussian case best predictors are linear and there is an extensive study of the asymptotics of asymptotically optimal esti mators. Some discussion of these classical results is given to provide a contrast with what may occur in the non-Gaussian case. There the prediction problem may be nonlinear and problems of estima tion can have a certain complexity due to the richer structure that non-Gaussian models may have. Gaussian stationary sequences have a reversible probability struc ture, that is, the probability structure with time increasing in the usual manner is the same as that with time reversed. Chapter 1 considers the question of reversibility for linear stationary sequences and gives necessary and sufficient conditions for the reversibility. A neat result of Breidt and Davis on reversibility is presented. A sim ple but elegant result of Cheng is also given that specifies conditions for the identifiability of the filter coefficients that specify a linear non-Gaussian random field. Seller Inventory # 9780387989174
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Springer-Verlag New York Inc., 1999
ISBN 10: 038798917X
ISBN 13: 9780387989174
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