Model Selection and Biased Estimation

Jonathon D. Brown
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Jonathon D. Brown: University of Washington, Department of Psychology

Chapter Chapter 8 in Advanced Statistics for the Behavioral Sciences, 2018, pp 253-288 from Springer

Abstract: 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.

Date: 2018
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DOI: 10.1007/978-3-319-93549-2_8

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