Gaussian and Non-Gaussian Linear Time Series and Random Fields (Springer Series in Statistics)

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9780387989174: Gaussian and Non-Gaussian Linear Time Series and Random Fields (Springer Series in Statistics)

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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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).

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"...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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