Journal
JOURNAL OF APPLIED STATISTICS
Volume 38, Issue 9, Pages 2043-2050Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/02664763.2010.545111
Keywords
model selection; least squares ratio; subset selection; information criteria
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Funding
- The Scientific and Technological Research Council of Turkey (TUBITAK)
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In this study, we evaluate several forms of both Akaike-type and Information Complexity (ICOMP)-type information criteria, in the context of selecting an optimal subset least squares ratio (LSR) regression model. Our simulation studies are designed to mimic many characteristics present in real data - heavy tails, multicollinearity, redundant variables, and completely unnecessary variables. Our findings are that LSR in conjunction with one of the ICOMP criteria is very good at selecting the true model. Finally, we apply these methods to the familiar body fat data set.
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