Discrimination Between Random and Fixed Effect Logistic Regression Models
D s- and KL-optimum designs are computed for discriminating between univariate logistic regression models with or without random effects. Both these competing optimum designs are constructed numerically. The main problem in finding them is the computation of some integrals at each step of the numerical procedure. In order to improve the convergence speed of this numerical procedure some integral approximations are suggested.
KeywordsLogistic Regression Model Directional Derivative Fisher Information Matrix Rival Model Univariate Logistic Regression Model
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This research was supported by the Spanish Junta de Castilla y León (Project ‘SA071A09’) and the Spanish Ministry of Education and Science (Projects ‘MTM 2007-672111- C03-01’ and ‘Ingenio Mathematica (i-MATH)’ No. CSD2006-00032, Consolider - Ingenio 2010).
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