BIAS in apparent classification rates in stepwise discriminant analysis
作者:
Alvin C. Rencher,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1992)
卷期:
Volume 21,
issue 2
页码: 373-389
ISSN:0361-0918
年代: 1992
DOI:10.1080/03610919208813024
出版商: Marcel Dekker, Inc.
关键词: subset selection;error rates;Monte Carlo
数据来源: Taylor
摘要:
When p variables in a discriminant analysis are chosen by stepwise selection, the mean and percentage points of the apparent correct classification rates are positively biased as compared to the setting where p variables are not selected from a larger set. This bias due to subset selection is examined by Monte Carlo methods and compared to the bias due solely to resubstitution of the original sample. Both types of bias are intensified when the number of variables exceeds the degrees of freedom for error.
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