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Model Selection and Biased Estimation

In: Advanced Statistics for the Behavioral Sciences

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  • Jonathon D. Brown

    (University of Washington, Department of Psychology)

Abstract

In Chap. 3 we learned that multiple regression is a powerful tool for modeling the contribution a variable makes to the prediction of a criterion holding other variables constant. Given this ability, it might seem that adding predictors to a regression model is always beneficial, but this is not the case. Part of the problem is collinearity. As discussed in Chap. 5 , when the predictors are strongly related, their regression coefficients become unstable and their standard errors become inflated. But even when collinearity is not an issue, adding predictors to a regression equation can sometimes do more harm than good. To understand why, we turn to a discussion of prediction error and model complexity.

Suggested Citation

  • Jonathon D. Brown, 2018. "Model Selection and Biased Estimation," Springer Books, in: Advanced Statistics for the Behavioral Sciences, chapter 0, pages 253-288, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-93549-2_8
    DOI: 10.1007/978-3-319-93549-2_8
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