Misspecified Mean Function Regression Making Good Use of Regression Models That Are Wrong  

Oleh:

Berk, Richard ; Brown, Lawrence ; Buja, Andreas ; George, Edward ; Pitkin, Emil ; Kai, Zhang ; Zhao, Linda

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Sociological Methods & Research (SMR) vol. 43 no. 03 (Aug. 2014), page 422-451.
Topik: Random Predictors; Linear Models; Model Misspecification; Regression Models; Misspecified Mean Function Regression

Ketersediaan

Perpustakaan PKPM Nomor Panggil: S28 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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There are over three decades of largely unrebutted criticism of regression analysis as practiced in the social sciences. Yet, regression analysis broadly construed remains for many the method of choice for characterizing conditional relationships. One possible explanation is that the existing alternatives sometimes can be seen by researchers as unsatisfying. In this article, we provide a different formulation. We allow the regression model to be incorrect and consider what can be learned nevertheless. To this end, the search for a correct model is abandoned. We offer instead a rigorous way to learn from regression approximations. These approximations, not “the truth,” are the estimation targets. There exist estimators that are asymptotically unbiased and standard errors that are asymptotically correct even when there are important specification errors. Both can be obtained easily from popular statistical packages.

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