Articoli correlati a Gaussian and Non-Gaussian Linear Time Series and Random...
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This monograph will be of interest to researchers and graduate students in time series and probability.
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Recensione
From the reviews:
SHORT BOOK REVIEWS
"...will make this book useful as a reference source to the more theoretical among time series specialists."
ZENTRALBLATT MATH
"This publication can be recommended to readers familiar with the basic concepts of time series who are interested in estimation problems in nonminimum phase processes."
Contenuti
1 Reversibility and Identifiability.- 1.1 Linear Sequences and the Gaussian Property.- 1.2 Reversibility.- 1.3 Identifiability.- 1.4 Minimum and Nonminimum Phase Sequences.- 2 Minimum Phase Estimation.- 2.1 The Minimum Phase Case and the Quasi-Gaussian Likelihood.- 2.2 Consistency.- 2.3 The Asymptotic Distribution.- 3 Homogeneous Gaussian Random Fields.- 3.1 Regular and Singular Fields.- 3.2 An Isometry.- 3.3 L-Fields and L-Markov Fields.- 4 Cumulants, Mixing and Estimation for Gaussian Fields.- 4.1 Moments and Cumulants.- 4.2 Higher Order Spectra.- 4.3 Some Simple Inequalities and Strong Mixing.- 4.4 Strong Mixing for Two-Sided Linear Processes.- 4.5 Mixing and a Central Limit Theorem for Random Fields.- 4.6 Estimation for Stationary Random Fields.- 4.7 Cumulants of Finite Fourier Transforms.- 4.8 Appendix: Two Inequalities.- 5 Prediction for Minimum and Nonminimum Phase Models.- 5.1 Introduction.- 5.2 A First Order Autoregressive Model.- 5.3 Nonminimum Phase Autoregressive Models.- 5.4 A Functional Equation.- 5.5 Entropy.- 5.6 Continuous Time Parameter Processes.- 6 The Fluctuation of the Quasi-Gaussian Likelihood.- 6.1 Initial Remarks.- 6.2 Derivation.- 6.3 The Limiting Process.- 7 Random Fields.- 7.1 Introduction.- 7.2 Markov Fields and Chains.- 7.3 Entropy and a Limit Theorem.- 7.4 Some Illustrations.- 8 Estimation for Possibly Nonminimum Phase Schemes.- 8.1 The Likelihood for Possibly Non-Gaussian Autoregressive Schemes.- 8.2 Asymptotic Normality.- 8.3 Preliminary Comments: Approximate Maximum Likelihood Estimates for Non-Gaussian Nonminimum Phase ARMA Sequences.- 8.4 The Likelihood Function.- 8.5 The Covariance Matrix.- 8.6 Solution of the Approximate Likelihood Equations.- 8.7 Cumulants and Estimation for Autoregressive Schemes.- 8.8 Superefficiency.- Bibliographic Notes.- References.- Notation.- Author Index.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
- EditoreSpringer
- Data di pubblicazione2012
- ISBN 10 1461270677
- ISBN 13 9781461270676
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero di pagine264
- Contatto del produttoreSpringer Nature Customer Service Center GmbH; ProductSafety@springernature.com
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The principal focus here is on autoregressive moving average models and analogous random fields, with probabilistic and statistical questions also being discussed. The book contrasts Gaussian models with noncausal or noninvertible (nonminimum phase) non-Gau. Codice articolo 4189698
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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ISBN 10: 1461270677
ISBN 13: 9781461270676
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The principal focus here is on autoregressive moving average models and analogous random fields, with probabilistic and statistical questions also being discussed. The book contrasts Gaussian models with noncausal or noninvertible (nonminimum phase) non-Gaussian models and deals with problems of prediction and estimation. New results for nonminimum phase non-Gaussian processes are exposited and open questions are noted. Intended as a text for gradutes in statistics, mathematics, engineering, the natural sciences and economics, the only recommendation is an initial background in probability theory and statistics. Notes on background, history and open problems are given at the end of the book. 264 pp. Englisch. Codice articolo 9781461270676
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Springer New York, Springer New York Sep 2012, 2012
ISBN 10: 1461270677
ISBN 13: 9781461270676
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 264 pp. Englisch. Codice articolo 9781461270676
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Codice articolo 9781461270676
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Gaussian and Non-Gaussian Linear Time Series and Random Fields (Springer Series in Statistics)
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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ISBN 10: 1461270677
ISBN 13: 9781461270676
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Gaussian and Non-Gaussian Linear Time Series and Random Fields (Springer Series in Statistics)
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