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Regression and time series model sel...

McQuarrie, Allan D. R.

Regression and time series model selection

紀錄類型:	書目-電子資源 : 單行本
正題名/作者:	Regression and time series model selection/ Allan D.R. McQuarrie, Chih-Ling Tsai.
作者:	McQuarrie, Allan D. R.
其他作者:	Tsai, Chih-Ling.
出版者:	Singapore ;World Scientific,c1998.
面頁冊數:	1 online resource (xxi, 455 p.) :ill.
標題:	Regression analysis. -
電子資源:	Click here for online access to this book (查閱全文) (EBSCO eBook)
ISBN:	9812385452 (electronic bk.)
ISBN:	9789812385451 (electronic bk.)

Regression and time series model selection
McQuarrie, Allan D. R.

Regression and time series model selection[electronic resource] /Allan D.R. McQuarrie, Chih-Ling Tsai. - Singapore ;World Scientific,c1998. - 1 online resource (xxi, 455 p.) :ill.

Includes bibliographical references (p. 430-439) and indexes.

Ch. 1. Introduction. 1.1. Background. 1.2. Overview. 1.3. Layout. 1.4. Topics not covered -- ch. 2. The univariate regression model. 2.1. Model description. 2.2. Derivations of the foundation model selection criteria. 2.3. Moments of model selection criteria. 2.4. Signal-to-noise corrected variants. 2.5. Overfitting. 2.6. Small-sample underfitting. 2.7. Random X regression and Monte Carlo study. 2.8. Summary -- ch. 3. The univariate autoregressive model. 3.1. Model description. 3.2. Selected derivations of model selection criteria. 3.3. Small-sample signal-to-noise ratios. 3.4. Overfitting. 3.5. Underfitting for two special case models. 3.6. Autoregressive Monte Carlo study. 3.7. Moving average MA(1) misspecified as autoregressive models. 3.8. Multistep forecasting models. 3.9. Summary -- ch. 4. The multivariate regression model. 4.1. Model description. 4.2. Selected derivations of model selection criteria. 4.3. Moments of model selection criteria. 4.4. Signal-to-noise corrected variants. 4.5. Overfitting properties. 4.6. Underfitting. 4.7. Monte Carlo study. 4.8. Summary -- ch. 5. The vector autoregressive model. 5.1. Model description. 5.2. Selected derivations of model selection criteria. 5.3. Small-sample signal-to-noise ratios. 5.4. Overfitting. 5.5. Underfitting in two special case models. 5.6. Vector autoregressive Monte Carlo study. 5.7. Summary -- ch. 6. Cross-validation and the bootstrap. 6.1. Univariate regression cross-validation. 6.2. Univariate autoregressive cross-validation. 6.3. Multivariate regression cross-validation. 6.4. Vector autoregressive cross-validation. 6.5. Univariate regression bootstrap. 6.6. Univariate autoregressive bootstrap. 6.7. Multivariate regression bootstrap. 6.8. Vector autoregressive bootstrap. 6.9. Monte Carlo study. 6.10. Summary -- ch. 7. Robust regression and quasi-likelihood. 7.1. Nonnormal error regression models. 7.2. Least absolute deviations regression. 7.3. Robust version of Cp. 7.4. Wald test version of Cp. 7.5. FPE for robust regression. 7.6. Unification of AIC criteria. 7.7. Quasi-likelihood. 7.8. Summary -- ch. 8. Nonparametric regression and wavelets. 8.1. Model selection in nonparametric regression. 8.2. Semiparametric regression model selection. 8.3. A cross-validatory AIC for hard wavelet thresholding. 8.4. Summary -- ch. 9. Simulations and examples. 9.1. Introduction. 9.2. Univariate regression models. 9.3. Autoregressive models. 9.4. Moving average MA(1) misspecified as autoregressive models. 9.5. Multivariate regression models. 9.6. Vector autoregressive models. 9.7. Summary.

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.

ISBN: 9812385452 (electronic bk.)Subjects--Topical Terms:

153780
Regression analysis.
Index Terms--Genre/Form:

172687
Electronic books.

LC Class. No.: QA278.2 / .M42 1998eb

Dewey Class. No.: 519.5/36

Regression and time series model selection
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245 1 0 $a Regression and time series model selection $h [electronic resource] / $c Allan D.R. McQuarrie, Chih-Ling Tsai.
260 $a Singapore ; $a River Edge, N.J. : $b World Scientific, $c c1998.
300 $a 1 online resource (xxi, 455 p.) : $b ill.
504 $a Includes bibliographical references (p. 430-439) and indexes.
505 0 $a Ch. 1. Introduction. 1.1. Background. 1.2. Overview. 1.3. Layout. 1.4. Topics not covered -- ch. 2. The univariate regression model. 2.1. Model description. 2.2. Derivations of the foundation model selection criteria. 2.3. Moments of model selection criteria. 2.4. Signal-to-noise corrected variants. 2.5. Overfitting. 2.6. Small-sample underfitting. 2.7. Random X regression and Monte Carlo study. 2.8. Summary -- ch. 3. The univariate autoregressive model. 3.1. Model description. 3.2. Selected derivations of model selection criteria. 3.3. Small-sample signal-to-noise ratios. 3.4. Overfitting. 3.5. Underfitting for two special case models. 3.6. Autoregressive Monte Carlo study. 3.7. Moving average MA(1) misspecified as autoregressive models. 3.8. Multistep forecasting models. 3.9. Summary -- ch. 4. The multivariate regression model. 4.1. Model description. 4.2. Selected derivations of model selection criteria. 4.3. Moments of model selection criteria. 4.4. Signal-to-noise corrected variants. 4.5. Overfitting properties. 4.6. Underfitting. 4.7. Monte Carlo study. 4.8. Summary -- ch. 5. The vector autoregressive model. 5.1. Model description. 5.2. Selected derivations of model selection criteria. 5.3. Small-sample signal-to-noise ratios. 5.4. Overfitting. 5.5. Underfitting in two special case models. 5.6. Vector autoregressive Monte Carlo study. 5.7. Summary -- ch. 6. Cross-validation and the bootstrap. 6.1. Univariate regression cross-validation. 6.2. Univariate autoregressive cross-validation. 6.3. Multivariate regression cross-validation. 6.4. Vector autoregressive cross-validation. 6.5. Univariate regression bootstrap. 6.6. Univariate autoregressive bootstrap. 6.7. Multivariate regression bootstrap. 6.8. Vector autoregressive bootstrap. 6.9. Monte Carlo study. 6.10. Summary -- ch. 7. Robust regression and quasi-likelihood. 7.1. Nonnormal error regression models. 7.2. Least absolute deviations regression. 7.3. Robust version of Cp. 7.4. Wald test version of Cp. 7.5. FPE for robust regression. 7.6. Unification of AIC criteria. 7.7. Quasi-likelihood. 7.8. Summary -- ch. 8. Nonparametric regression and wavelets. 8.1. Model selection in nonparametric regression. 8.2. Semiparametric regression model selection. 8.3. A cross-validatory AIC for hard wavelet thresholding. 8.4. Summary -- ch. 9. Simulations and examples. 9.1. Introduction. 9.2. Univariate regression models. 9.3. Autoregressive models. 9.4. Moving average MA(1) misspecified as autoregressive models. 9.5. Multivariate regression models. 9.6. Vector autoregressive models. 9.7. Summary.
520 $a 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.
588 $a Description based on print version record.
650 0 $a Regression analysis. $3 153780
650 0 $a Time-series analysis. $3 146816
650 0 $a Mathematical models. $3 148622
650 7 $a MATHEMATICS $x Probability & Statistics $x Regression Analysis. $2 bisacsh $3 1000066807
655 4 $a Electronic books. $2 local. $3 172687
700 1 $a Tsai, Chih-Ling. $3 1000068546
776 0 8 $i Print version: $a McQuarrie, Allan D.R. $t Regression and time series model selection. $d Singapore ; River Edge, N.J. : World Scientific, c1998 $z 981023242X $w (DLC) 98190995 $w (OCoLC)39719241
856 4 0 $3 EBSCOhost $u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=91492 $z Click here for online access to this book (查閱全文) (EBSCO eBook)
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