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Gaussian and Non-Gaussian Linear Time Series and Random Fields (Springer Series in Statistics) - Softcover
Synopsis
This monograph will be of interest to researchers and graduate students in time series and probability.
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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."
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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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ISBN 10: 1461270677
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 264 pp. Englisch. Seller Inventory # 9781461270676
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Taschenbuch. 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 # 9781461270676
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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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 # 4189698
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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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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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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 264 pp. Englisch. Seller Inventory # 9781461270676
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