principal components regression, PCA, factor analysis, Big Data, data reduction
Principal components regression (PCR) reduces a large number of explanatory variables 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.
Artigue, Heidi Margaret and Artigue, Heidi Margaret, "The Principal Problem with Principal Components Regression" (2018). Pomona Faculty Publications and Research. 495.