Document Type
Article
Department
Economics (Pomona)
Publication Date
12-12-2018
Keywords
principal components regression, PCA, factor analysis, Big Data, data reduction
Abstract
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.
Rights Information
© 2019 The Author(s)
Terms of Use & License Information
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Artigue, Heidi Margaret and Smith, Gary, "The Principal Problem with Principal Components Regression" (2018). Pomona Faculty Publications and Research. 495.
https://scholarship.claremont.edu/pomona_fac_pub/495
Comments
https://www-tandfonline-com.ccl.idm.oclc.org/doi/full/10.1080/25742558.2019.1622190
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.