Assumption Lean Regression
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2018
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911073230127104 |
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| author | Berk, Richard A. Buja, Andreas Brown, Lawrence George, Edward Kuchibhotla, Arun Kumar Su, Weijie J. Zhao, Linda |
| author_facet | Berk, Richard A. Buja, Andreas Brown, Lawrence George, Edward Kuchibhotla, Arun Kumar Su, Weijie J. Zhao, Linda |
| contents | It is well known that models used in conventional regression analysis are commonly misspecified. A standard response is little more than a shrug. Data analysts invoke Box's maxim that all models are wrong and then proceed as if the results are useful nevertheless. In this paper, we provide an alternative. Regression models are treated explicitly as approximations of a true response surface that can have a number of desirable statistical properties, including estimates that are asymptotically unbiased. Valid statistical inference follows. We generalize the formulation to include regression functionals, which broadens substantially the range of potential applications. An empirical application is provided to illustrate the paper's key concepts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1806_09014 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Assumption Lean Regression Berk, Richard A. Buja, Andreas Brown, Lawrence George, Edward Kuchibhotla, Arun Kumar Su, Weijie J. Zhao, Linda Methodology It is well known that models used in conventional regression analysis are commonly misspecified. A standard response is little more than a shrug. Data analysts invoke Box's maxim that all models are wrong and then proceed as if the results are useful nevertheless. In this paper, we provide an alternative. Regression models are treated explicitly as approximations of a true response surface that can have a number of desirable statistical properties, including estimates that are asymptotically unbiased. Valid statistical inference follows. We generalize the formulation to include regression functionals, which broadens substantially the range of potential applications. An empirical application is provided to illustrate the paper's key concepts. |
| title | Assumption Lean Regression |
| topic | Methodology |
| url | https://arxiv.org/abs/1806.09014 |