Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Gaussian and Non-Gaussian Linear Time Series and Random Fields

Gaussian and Non-Gaussian Linear Time Series and Random Fields


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About the Book

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.


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Product Details
  • ISBN-13: 9780387989174
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Depth: 0.25
  • Height: 243 mm
  • No of Pages: 247
  • Series Title: Springer Statistics
  • Weight: 603 gr
  • ISBN-10: 038798917X
  • Publisher Date: 21 Dec 1999
  • Binding: Hardback
  • Edition: 2000 ed.
  • Language: English
  • Returnable: N
  • Spine Width: 17 mm
  • Width: 162 mm


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