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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