| Description |
1 online resource (xxi, 455 pages) : illustrations |
| Contents |
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 |
| 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 |
| Bibliography |
Includes bibliographical references (pages 430-439) and indexes |
| Notes |
Print version record |
| Subject |
Regression analysis.
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Time-series analysis.
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Mathematical models.
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Regression Analysis
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Models, Theoretical
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mathematical models.
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MATHEMATICS -- Probability & Statistics -- Regression Analysis.
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Mathematical models
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Regression analysis
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Time-series analysis
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Tidsserieanalys.
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Matematisk statistik.
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| Form |
Electronic book
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| Author |
Tsai, Chih-Ling
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| LC no. |
98190995 |
| ISBN |
9812385452 |
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9789812385451 |
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981023242X |
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9789810232429 |
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