Model selection using information criteria under a new estimation method: least squares ratio |
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| Authors: | Eylem Deniz Oguz Akbilgic J Andrew Howe |
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| Institution: | 1. Department of Statistics, Faculty of Science and Letters , Mimar Sinan Fine Arts University , Istanbul, Turkey;2. Department of Quantitative Techniques, Faculty of Business Administration , Istanbul University , Istanbul, Turkey;3. Tennessee Valley Authority , Tennessee, USA |
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| Abstract: | 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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| Keywords: | model selection least squares ratio subset selection information criteria |
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