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Synopsis
This monograph will be of interest to researchers and graduate students in time series and probability.
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Présentation de l'éditeur
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.
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- ÉditeurSpringer-Verlag New York Inc.
- Date d'édition1999
- ISBN 10 038798917X
- ISBN 13 9780387989174
- ReliureRelié
- Langueanglais
- Nombre de pages247
- Coordonnées du fabricantnon disponible
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Gaussian and Non-Gaussian Linear Time Series and Random Fields.
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New York, NY, U.S.A. Springer Verlag, 1999
ISBN 10 : 038798917X
ISBN 13 : 9780387989174
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Vendeur : CSG Onlinebuch GMBH, Darmstadt, Allemagne
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Gebunden. Etat : Sehr gut. Gebraucht - Sehr gut Zustand: Sehr gut, XIII, 246 pp. About this book 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. Written for researchers, graduate students. N° de réf. du vendeur 18330
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Gaussian and Non-Gaussian Linear Time Series and Random Fields (Springer Series in Statistics)
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Springer, 2000
ISBN 10 : 038798917X
ISBN 13 : 9780387989174
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Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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Hardcover. Etat : Like New. Like NewLIKE NEW. book. N° de réf. du vendeur ERICA837038798917X3
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
Edité par
Springer, 1999
ISBN 10 : 038798917X
ISBN 13 : 9780387989174
Ancien ou d'occasion
Couverture rigide
Vendeur : ThriftBooks-Atlanta, AUSTELL, GA, Etats-Unis
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Hardcover. Etat : Very Good. No Jacket. Former library book; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 1.13. N° de réf. du vendeur G038798917XI4N10
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Springer New York, 1999
ISBN 10 : 038798917X
ISBN 13 : 9780387989174
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Vendeur : moluna, Greven, Allemagne
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Gebunden. Etat : 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. N° de réf. du vendeur 5913570
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Springer New York Dez 1999, 1999
ISBN 10 : 038798917X
ISBN 13 : 9780387989174
Neuf
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 268 pp. Englisch. N° de réf. du vendeur 9780387989174
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Springer New York, Springer US Dez 1999, 1999
ISBN 10 : 038798917X
ISBN 13 : 9780387989174
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. 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 268 pp. Englisch. N° de réf. du vendeur 9780387989174
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Gaussian and Non-Gaussian Linear Time Series and Random Fields (Springer Series in Statistics)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
Edité par
Springer New York, Springer US, 1999
ISBN 10 : 038798917X
ISBN 13 : 9780387989174
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : 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. N° de réf. du vendeur 9780387989174
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
Edité par
Springer-Verlag New York Inc., 1999
ISBN 10 : 038798917X
ISBN 13 : 9780387989174
Neuf
Couverture rigide
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
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Hardback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 583. N° de réf. du vendeur C9780387989174
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Gaussian and Non-Gaussian Linear Time Series and Random Fields (Springer Series in Statistics)
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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