Regression and time series model selection:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
World Scientific
©1998
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| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 Volltext |
| Beschreibung: | Includes bibliographical references (pages 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 |
| Beschreibung: | 1 Online-Ressource (xxi, 455 pages) |
| ISBN: | 9789812385451 9812385452 |
Internformat
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| 100 | 1 | |a McQuarrie, Allan D. R. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Regression and time series model selection |c Allan D.R. McQuarrie, Chih-Ling Tsai |
| 264 | 1 | |a Singapore |b World Scientific |c ©1998 | |
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| 500 | |a Includes bibliographical references (pages 430-439) and indexes | ||
| 500 | |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 -- | ||
| 500 | |a - 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 -- | ||
| 500 | |a - 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 -- | ||
| 500 | |a - 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 | ||
| 500 | |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 | ||
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Datensatz im Suchindex
| _version_ | 1804175561578250240 |
|---|---|
| any_adam_object | |
| author | McQuarrie, Allan D. R. |
| author_facet | McQuarrie, Allan D. R. |
| author_role | aut |
| author_sort | McQuarrie, Allan D. R. |
| author_variant | a d r m adr adrm |
| building | Verbundindex |
| bvnumber | BV043126637 |
| collection | ZDB-4-EBA |
| ctrlnum | (OCoLC)52859244 (DE-599)BVBBV043126637 |
| dewey-full | 519.5/36 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/36 |
| dewey-search | 519.5/36 |
| dewey-sort | 3519.5 236 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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| id | DE-604.BV043126637 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:18:15Z |
| institution | BVB |
| isbn | 9789812385451 9812385452 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028550828 |
| oclc_num | 52859244 |
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| owner_facet | DE-1046 DE-1047 |
| physical | 1 Online-Ressource (xxi, 455 pages) |
| psigel | ZDB-4-EBA ZDB-4-EBA FAW_PDA_EBA |
| publishDate | 1998 |
| publishDateSearch | 1998 |
| publishDateSort | 1998 |
| publisher | World Scientific |
| record_format | marc |
| spelling | McQuarrie, Allan D. R. Verfasser aut Regression and time series model selection Allan D.R. McQuarrie, Chih-Ling Tsai Singapore World Scientific ©1998 1 Online-Ressource (xxi, 455 pages) txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references (pages 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 MATHEMATICS / Probability & Statistics / Regression Analysis bisacsh Mathematical models fast Regression analysis fast Time-series analysis fast Mathematisches Modell Regression analysis Time-series analysis Mathematical models Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Zeitreihe (DE-588)4127298-5 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 s Zeitreihe (DE-588)4127298-5 s 1\p DE-604 Zeitreihenanalyse (DE-588)4067486-1 s 2\p DE-604 Tsai, Chih-Ling Sonstige oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=91492 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | McQuarrie, Allan D. R. Regression and time series model selection MATHEMATICS / Probability & Statistics / Regression Analysis bisacsh Mathematical models fast Regression analysis fast Time-series analysis fast Mathematisches Modell Regression analysis Time-series analysis Mathematical models Zeitreihenanalyse (DE-588)4067486-1 gnd Zeitreihe (DE-588)4127298-5 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| subject_GND | (DE-588)4067486-1 (DE-588)4127298-5 (DE-588)4129903-6 |
| title | Regression and time series model selection |
| title_auth | Regression and time series model selection |
| title_exact_search | Regression and time series model selection |
| title_full | Regression and time series model selection Allan D.R. McQuarrie, Chih-Ling Tsai |
| title_fullStr | Regression and time series model selection Allan D.R. McQuarrie, Chih-Ling Tsai |
| title_full_unstemmed | Regression and time series model selection Allan D.R. McQuarrie, Chih-Ling Tsai |
| title_short | Regression and time series model selection |
| title_sort | regression and time series model selection |
| topic | MATHEMATICS / Probability & Statistics / Regression Analysis bisacsh Mathematical models fast Regression analysis fast Time-series analysis fast Mathematisches Modell Regression analysis Time-series analysis Mathematical models Zeitreihenanalyse (DE-588)4067486-1 gnd Zeitreihe (DE-588)4127298-5 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | MATHEMATICS / Probability & Statistics / Regression Analysis Mathematical models Regression analysis Time-series analysis Mathematisches Modell Zeitreihenanalyse Zeitreihe Regressionsanalyse |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=91492 |
| work_keys_str_mv | AT mcquarrieallandr regressionandtimeseriesmodelselection AT tsaichihling regressionandtimeseriesmodelselection |