An Analytic Variable Selection Technique for Principal Component Regression.

摘要

This paper presents an analytic technique for deleting predictor variables from a linear regression model when principal components of X'X are removed to adjust for multicollinearities in the data. The technique can be adapted to commonly used variable selection procedures such as backward elimination to eliminate redundant predictor variables without appreciably increasing the residual sum of squares.