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The cover of “Gaussian and Non-Gaussian Linear Time Series and Random Fields” - Open Library.

"Gaussian and Non-Gaussian Linear Time Series and Random Fields" was published by Springer New York in 2000 - New York, NY, it has 247 pages and the language of the book is English.


“Gaussian and Non-Gaussian Linear Time Series and Random Fields” Metadata:

  • Title: ➤  Gaussian and Non-Gaussian Linear Time Series and Random Fields
  • Author:
  • Language: English
  • Number of Pages: 247
  • Publisher: Springer New York
  • Publish Date:
  • Publish Location: New York, NY

“Gaussian and Non-Gaussian Linear Time Series and Random Fields” Subjects and Themes:

Edition Specifications:

  • Format: [electronic resource] /
  • Pagination: ➤  1 online resource (xiii, 247 p.)

Edition Identifiers:

AI-generated Review of “Gaussian and Non-Gaussian Linear Time Series and Random Fields”:


"Gaussian and Non-Gaussian Linear Time Series and Random Fields" Table Of Contents:

  • 1- Reversibility and Identifiability
  • 2- Minimum Phase Estimation
  • 3- Homogeneous Gaussian Random Fields
  • 4- Cumulants, Mixing and Estimation for Gaussian Fields
  • 5- Prediction for Minimum and Nonminimum Phase Models
  • 6- The Fluctuation of the quasi-Gaussian Likelihood
  • 7- Random Fields
  • 8- Estimation for Possibly Nonminimum Phase Schemes.

"Gaussian and Non-Gaussian Linear Time Series and Random Fields" Description:

The Open Library:

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. 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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