Does anyone know of a good reference for using principal components as a
subset selection method in regression, not so much to reduce
collinearity, but rather to reduce the number of variables?
There are intriguing hints in several books (e.g. Harrell Regression
modelling strategies; Miller Subset selection in regression, but I was
wondering if anyone knew of a good paper or two (or a good book) on
using this method.
TIA
Peter
Peter L. Flom, PhD
Assistant Director, Statistics and Data Analysis Core
Center for Drug Use and HIV Research
National Development and Research Institutes
71 W. 23rd St
New York, NY 10010
(212) 845-4485 (voice)
(917) 438-0894 (fax)
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