Assumption Lean Regression.
It is well known that with observational data, models used in conventional regression analyses are commonly misspecified. Yet in practice, one tends to proceed with interpretations and inferences that rely on correct specification. Even those who invoke Box's maxim that all models are wrong proceed...
| Publicado en: | American Statistician Vol. 75; no. 1; pp. 76 - 85 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Feb2021
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=148278884&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 148278884 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Feb2021 vid: 75 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 148278884 10.1080/00031305.2019.1592781 ppf: 76 ppct: 9 formats: tig: atl: Assumption Lean Regression. aug: au: Berk, Richard Buja, Andreas Brown, Lawrence George, Edward Kuchibhotla, Arun Kumar Su, Weijie Zhao, Linda affil: Department of Criminology, University of Pennsylvania, Philadelphia, PA Department of Statistics, University of Pennsylvania, Philadelphia, PA su: Regression analysis Functionals sug: subj: Regression analysis Functionals keyword: Foundational issues Generalized linear models Linear regression Misspecified regression models Regression functionals Foundational issues Generalized linear models Linear regression Misspecified regression models Regression functionals ab: It is well known that with observational data, models used in conventional regression analyses are commonly misspecified. Yet in practice, one tends to proceed with interpretations and inferences that rely on correct specification. Even those who invoke Box's maxim that all models are wrong proceed as if results were generally useful. Misspecification, however, has implications that affect practice. Regression models are approximations to a true response surface and should be treated as such. Accordingly, regression parameters should be interpreted as statistical functionals. Importantly, the regressor distribution affects targets of estimation and regressor randomness affects the sampling variability of estimates. As a consequence, inference should be based on sandwich estimators or the pairs (x–y) bootstrap. Traditional prediction intervals lose their pointwise coverage guarantees, but empirically calibrated intervals can be justified for future populations. We illustrate the key concepts with an empirical application. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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