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Model selection using information criteria under a new estimation method: least squares ratio

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  • Eylem Deniz
  • Oguz Akbilgic
  • J. Andrew Howe

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.

Suggested Citation

  • Eylem Deniz & Oguz Akbilgic & J. Andrew Howe, 2011. "Model selection using information criteria under a new estimation method: least squares ratio," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 2043-2050, November.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:2043-2050
    DOI: 10.1080/02664763.2010.545111
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    References listed on IDEAS

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    1. Bozdogan, Hamparsum & Haughton, Dominique M. A., 1998. "Informational complexity criteria for regression models," Computational Statistics & Data Analysis, Elsevier, vol. 28(1), pages 51-76, July.
    2. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    3. Xinli Bao & Hamparsum Bozdogan & Vuttichai Chatpattananan & Kenneth Gilbert, 2005. "An implicit enumeration algorithm for mining high dimensional data," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 1(1/2), pages 123-144.
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