The principal problem with principal components regression

被引：26

作者：

Artigue, Heidi ^{[1
]}

Smith, Gary ^{[1
]}

机构：

[1] Pomona Coll, Dept Econ, 425 N Coll Ave, Claremont, CA 91711 USA

来源：

COGENT MATHEMATICS & STATISTICS | 2019年 / 6卷

关键词：

principal components regression; PCA; factor analysis; Big Data; data reduction; VARIABLES; MODELS; NUMBER;

D O I：

10.1080/25742558.2019.1622190

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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.

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页数：11

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