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
Discrimination between random and fixed effect logistic regression models / C. Tommasi, M.T. Santos Martin, J.M. Rodriguez Diaz - In: mODa9-advances in model-oriented design and analysis / [a cura di] A. Giovagnoli, A.C. Atkinson, B. Torsney, C. May. - Heidelberg : Physica, 2010. - ISBN 978-3-7908-2409-4. - pp. 205-212 (( Intervento presentato al 9. convegno mODa : model-oriented data analysis and optimum design tenutosi a Bertinoro nel 2010.
Discrimination between random and fixed effect logistic regression models
C. TommasiPrimo
;
2010
Abstract
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
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