Overview

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.
ISBN-13

9780387989174

ISBN-10

038798917X

Weight

2.69 Pounds

Dimensions

9.25 x 6.10 x 0.27 In

List Price

$109.99

Format

Hardcover

Language

English

Pages

xiii, 247 pages

Publisher

Published On

1999-12-21


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Alibris
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Sparks, NV, USA

Print on demand Sewn binding. Cloth over boards. 247 p. Contains: Unspecified. Springer Statistics.
$112.98

 Free delivery by: 30 Mar 2026


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