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Series de tiempo lineales gaussianas y no gaussianas y campos aleatorios, M. Rosenblatt-
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N.º de artículo de eBay:364874569778
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Características del artículo
- Estado
- En muy buen estado
- Notas del vendedor
- ISBN
- 9780387989174
- Subject Area
- Mathematics
- Publication Name
- Gaussian and Non-Gaussian Linear Time Series and Random Fields
- Publisher
- Springer New York
- Item Length
- 9.3 in
- Subject
- Probability & Statistics / Stochastic Processes, Probability & Statistics / General
- Publication Year
- 1999
- Series
- Springer Series in Statistics Ser.
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 0.3 in
- Item Weight
- 43 Oz
- Item Width
- 6.1 in
- Number of Pages
- Xiii, 247 Pages
Acerca de este producto
Product Identifiers
Publisher
Springer New York
ISBN-10
038798917X
ISBN-13
9780387989174
eBay Product ID (ePID)
21038298013
Product Key Features
Number of Pages
Xiii, 247 Pages
Publication Name
Gaussian and Non-Gaussian Linear Time Series and Random Fields
Language
English
Publication Year
1999
Subject
Probability & Statistics / Stochastic Processes, Probability & Statistics / General
Type
Textbook
Subject Area
Mathematics
Series
Springer Series in Statistics Ser.
Format
Hardcover
Dimensions
Item Height
0.3 in
Item Weight
43 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
99-042811
Reviews
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.", 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.", 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."
Dewey Edition
21
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.5/5
Table Of Content
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.
Synopsis
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., This monograph will be of interest to researchers and graduate students in time series and probability., This book is concerned with linear time series and random fields in both the Gaussian and especially the non-Gaussian context focusing on autoregressive moving average models and analogous random fields. The book also deals with problems of prediction and estimation, discussing both the probabilistic and statistical questions that arise in each. Included are notes on the subjects' background and history, new results for nonminimum phase non-Gaussian processes, and open questions. The book is intended for users in statistics, mathematics, engineering, the natural sciences, and economics., 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.
LC Classification Number
QA273.A1-274.9
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