Description: Springer Series in Statistics Ser.: Gaussian and Non-Gaussian Linear Time Series and Random Fields, Murray Rosenblatt Much of this book is concerned with autoregressive and moving average 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 estimators. 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 estimation can have a certain complexity due to the richer structure that non-Gaussian models may have. Gaussian stationary sequences have a reversible probability structure, 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 simple 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. Will ship via USPS Media Mail
Price: 19.95 USD
Location: Tampa, Florida
End Time: 2025-01-03T23:16:13.000Z
Shipping Cost: 5.38 USD
Product Images
Item Specifics
All returns accepted: ReturnsNotAccepted
Item Length: 9.3in
Item Height: 0.3in
Item Width: 6.1in
Author: Murray Rosenblatt
Publication Name: Gaussian and Non-Gaussian Linear Time Series and Random Fields
Format: Hardcover
Language: English
Publisher: Springer New York
Series: Springer Series in Statistics Ser.
Publication Year: 1999
Type: Textbook
Item Weight: 43 Oz
Number of Pages: Xiii, 247 Pages
