Frequency of Selecting Noise Variables in Subset Regression Analysis: A Simulation Study

Abstract
This article presents the results of a simulation study of variable selection in a multiple regression context that evaluates the frequency of selecting noise variables and the bias of the adjusted R 2 of the selected variables when some of the candidate variables are authentic. It is demonstrated that for most samples a large percentage of the selected variables is noise, particularly when the number of candidate variables is large relative to the number of observations. The adjusted R 2 of the selected variables is highly inflated.

This publication has 1 reference indexed in Scilit:

  • Data Mining
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