Regression and Time Series Model Selection
By addebook • Mar 26th, 2009 • Category: Physics
Regression and Time Series Model Selection
by: Allan D. R. McQuarrie, Chih-Ling Tsai
Regression and Time Series Model Selection
By Allan D. R. McQuarrie, Chih-Ling Tsai
Publisher: World Scientific Publishing Company
Number Of Pages: 455
Publication Date: 1998-06
ISBN-10 / ASIN: 981023242X
ISBN-13 / EAN: 9789810232429
Product Description:
This important book describes procedures for selecting a model from a large set of competing statistical models. It includes model selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including wavelets) and semiparametric regression models, and quasi-likelihood and robust regression models. Information-based model selection criteria are discussed, and small sample and asymptotic properties are presented. The book also provides examples and large scale simulation studies comparing the performances of information-based model selection criteria, bootstrapping, and cross-validation selection methods over a wide range of models.
Summary: comprehensive treatment for time series
Rating: 4
It is too bad that this book was not published by one of the major publishers of statistics books. It is a very important but largely overlooked treatise on model selection procedures for regression and time series. The authors are experts in this area who have made important contributions to the theory. There is a wealth of techniques for model selection and there has been much confusion about their properties and usefulness. This book covers most of the methods, is very much up-to-date and clears up much of the confusion right in the first chapter!
Techniques for univariate regression, autoregressive time series models, multivariate regression, vector autoregression, cross-validation, bootstrap, robust regression, nonparametric regression and wavelets are all covered. Many practical examples are given to illustrate the methods and there are also a number of useful simulation studies that appear in the book. The final chapter (Chapter 9) covers extensive simulations comparing many of the popular model selection criteria for both time series and regresion modeling.
My only disappointment is the omission of the recent developments in Bayesian model selection. At least the authors mention this omission upfront in Chapter 1 and provide good references to the literature.
Summary: comprehensive treatment of model selection methods
Rating: 4
It is too bad that this book was not published by one of the major publishers of statistics books. It is a very important but largely overlooked treatise on model selection procedures for regression and time series. The authors are experts in this area who have made important contributions to the theory. There is a wealth of techniques for model selection and there has been much confusion about their properties and usefulness. This book covers most of the methods, is very much up-to-date and clears up much of the confusion right in the first chapter!
Techniques for univariate regression, autoregressive time series models, multivariate regression, vector autoregression, cross-validation, bootstrap, robust regression, nonparametric regression and wavelets are all covered. Many practical examples are given to illustrate the methods and there are also a number of useful simulation studies that appear in the book. The final chapter (Chapter 9) covers extensive simulations comparing many of the popular model selection criteria for both time series and regresion modeling.
My only disappointment is the omission of the recent developments in Bayesian model selection. At least the authors mention this omission upfront in Chapter 1 and provide good references to the literature.
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