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

Rosenblatt, Murray

Hardcover

ISBN 10: 038798917X ISBN 13: 9780387989174

Verlag: Springer, 1999

Inhaltsangabe

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.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 1999
• Sprache: Englisch
• ISBN 10: 038798917X
• ISBN 13: 9780387989174
• Einband: Gebundene Ausgabe
• Anzahl der Seiten: 260
• Kontakt zum Hersteller: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Deutschland