Regression and time series model selection /:
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 semipar...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore ; River Edge, N.J. :
World Scientific,
©1998.
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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 resource (xxi, 455 pages :) |
| Bibliographie: | Includes bibliographical references (pages 430-439) and indexes. |
| ISBN: | 9812385452 9789812385451 981023242X 9789810232429 |
Internformat
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| 100 | 1 | |a McQuarrie, Allan D. R. | |
| 245 | 1 | 0 | |a Regression and time series model selection / |c Allan D.R. McQuarrie, Chih-Ling Tsai. |
| 260 | |a Singapore ; |a River Edge, N.J. : |b World Scientific, |c ©1998. | ||
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| 504 | |a Includes bibliographical references (pages 430-439) and indexes. | ||
| 588 | 0 | |a Print version record. | |
| 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. | ||
| 650 | 0 | |a Regression analysis. |0 http://id.loc.gov/authorities/subjects/sh85112392 | |
| 650 | 0 | |a Time-series analysis. |0 http://id.loc.gov/authorities/subjects/sh85135430 | |
| 650 | 0 | |a Mathematical models. |0 http://id.loc.gov/authorities/subjects/sh85082124 | |
| 650 | 2 | |a Regression Analysis |0 https://id.nlm.nih.gov/mesh/D012044 | |
| 650 | 2 | |a Models, Theoretical |0 https://id.nlm.nih.gov/mesh/D008962 | |
| 650 | 6 | |a Analyse de régression. | |
| 650 | 6 | |a Série chronologique. | |
| 650 | 6 | |a Modèles mathématiques. | |
| 650 | 7 | |a mathematical models. |2 aat | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x Regression Analysis. |2 bisacsh | |
| 650 | 7 | |a Mathematical models |2 fast | |
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| 650 | 7 | |a Time-series analysis |2 fast | |
| 650 | 7 | |a Tidsserieanalys. |2 sao | |
| 650 | 7 | |a Matematisk statistik. |2 sao | |
| 700 | 1 | |a Tsai, Chih-Ling. | |
| 758 | |i has work: |a Regression and time series model selection (Work) |1 https://id.oclc.org/worldcat/entity/E39PCYcWjDjCV3dHHCxwYtV8JC |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 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, ©1998 |z 981023242X |w (DLC) 98190995 |w (OCoLC)39719241 |
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| 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. |
| ctrlnum | (OCoLC)52859244 |
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| discipline | Mathematik |
| format | Electronic eBook |
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McQuarrie, Chih-Ling Tsai.</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Singapore ;</subfield><subfield code="a">River Edge, N.J. :</subfield><subfield code="b">World Scientific,</subfield><subfield code="c">©1998.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xxi, 455 pages :)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references (pages 430-439) and indexes.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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. 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| id | ZDB-4-EBA-ocm52859244 |
| illustrated | Illustrated |
| indexdate | 2024-10-25T16:15:58Z |
| institution | BVB |
| isbn | 9812385452 9789812385451 981023242X 9789810232429 |
| language | English |
| lccn | 98190995 |
| oclc_num | 52859244 |
| open_access_boolean | |
| owner | MAIN |
| owner_facet | MAIN |
| physical | 1 online resource (xxi, 455 pages :) |
| psigel | ZDB-4-EBA |
| publishDate | 1998 |
| publishDateSearch | 1998 |
| publishDateSort | 1998 |
| publisher | World Scientific, |
| record_format | marc |
| spelling | McQuarrie, Allan D. R. Regression and time series model selection / Allan D.R. McQuarrie, Chih-Ling Tsai. Singapore ; River Edge, N.J. : World Scientific, ©1998. 1 online resource (xxi, 455 pages :) text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references (pages 430-439) and indexes. Print version record. 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. Regression analysis. http://id.loc.gov/authorities/subjects/sh85112392 Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Mathematical models. http://id.loc.gov/authorities/subjects/sh85082124 Regression Analysis https://id.nlm.nih.gov/mesh/D012044 Models, Theoretical https://id.nlm.nih.gov/mesh/D008962 Analyse de régression. Série chronologique. Modèles mathématiques. mathematical models. aat MATHEMATICS Probability & Statistics Regression Analysis. bisacsh Mathematical models fast Regression analysis fast Time-series analysis fast Tidsserieanalys. sao Matematisk statistik. sao Tsai, Chih-Ling. has work: Regression and time series model selection (Work) https://id.oclc.org/worldcat/entity/E39PCYcWjDjCV3dHHCxwYtV8JC https://id.oclc.org/worldcat/ontology/hasWork Print version: McQuarrie, Allan D.R. Regression and time series model selection. Singapore ; River Edge, N.J. : World Scientific, ©1998 981023242X (DLC) 98190995 (OCoLC)39719241 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=91492 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=91492 Volltext |
| spellingShingle | McQuarrie, Allan D. R. Regression and time series model selection / 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. Regression analysis. http://id.loc.gov/authorities/subjects/sh85112392 Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Mathematical models. http://id.loc.gov/authorities/subjects/sh85082124 Regression Analysis https://id.nlm.nih.gov/mesh/D012044 Models, Theoretical https://id.nlm.nih.gov/mesh/D008962 Analyse de régression. Série chronologique. Modèles mathématiques. mathematical models. aat MATHEMATICS Probability & Statistics Regression Analysis. bisacsh Mathematical models fast Regression analysis fast Time-series analysis fast Tidsserieanalys. sao Matematisk statistik. sao |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85112392 http://id.loc.gov/authorities/subjects/sh85135430 http://id.loc.gov/authorities/subjects/sh85082124 https://id.nlm.nih.gov/mesh/D012044 https://id.nlm.nih.gov/mesh/D008962 |
| 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 | Regression analysis. http://id.loc.gov/authorities/subjects/sh85112392 Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Mathematical models. http://id.loc.gov/authorities/subjects/sh85082124 Regression Analysis https://id.nlm.nih.gov/mesh/D012044 Models, Theoretical https://id.nlm.nih.gov/mesh/D008962 Analyse de régression. Série chronologique. Modèles mathématiques. mathematical models. aat MATHEMATICS Probability & Statistics Regression Analysis. bisacsh Mathematical models fast Regression analysis fast Time-series analysis fast Tidsserieanalys. sao Matematisk statistik. sao |
| topic_facet | Regression analysis. Time-series analysis. Mathematical models. Regression Analysis Models, Theoretical Analyse de régression. Série chronologique. Modèles mathématiques. mathematical models. MATHEMATICS Probability & Statistics Regression Analysis. Mathematical models Regression analysis Time-series analysis Tidsserieanalys. Matematisk statistik. |
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| work_keys_str_mv | AT mcquarrieallandr regressionandtimeseriesmodelselection AT tsaichihling regressionandtimeseriesmodelselection |