The principal problem with principal components regression. Issue 1 (1st January 2019) - Artigue, Heidi; Smith, Gary
Abstract: Principal components regression (PCR) reduces a large number of explanatory variables in a regression model down to a small number of principal components. PCR is thought to be more useful, the more numerous the potential explanatory variables. The reality is that a large number of candidate explanatory variables does not make PCR more valuable; instead, it magnifies the failings of PCR.