Variable selection and transformation in linear regression models
AbstractWe 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 72 (2005)
Issue (Month): 3 (May)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- 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.
- Pasaran, M.H. & Pasaran, B., 1989. "A Simulation Approach To The Problem Of Computing Cox'S Statistic For Testing Non-Nested Models," Papers 7, California Los Angeles - Applied Econometrics.
- 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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