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Variable selection and transformation in linear regression models

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  • Yeo, In-Kwon

Abstract

We develop a method for comparing separate linear models, for a common response variable that may be expressed on different scales and may be described by distinct explanatory variables. A method of stochastic simulation is used to approximate the fitted maximum likelihood estimates and then the Cox statistic is computed to test separate linear models. The bootstrap iteration is also used to calibrate confidence intervals to correct the test level.

Suggested Citation

  • Yeo, In-Kwon, 2005. "Variable selection and transformation in linear regression models," Statistics & Probability Letters, Elsevier, vol. 72(3), pages 219-226, May.
  • Handle: RePEc:eee:stapro:v:72:y:2005:i:3:p:219-226
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    References listed on IDEAS

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    1. Hashem Pesaran, M. & Pesaran, Bahram, 1993. "A simulation approach to the problem of computing Cox's statistic for testing nonnested models," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 377-392.
    2. White, Halbert, 1982. "Regularity conditions for cox's test of non-nested hypotheses," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 301-318, August.
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