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Showing 2 results for Niaparast
Mehrdad Niaparast, Sahar Mehr-Mansour,
Volume 4, Issue 1 (9-2010)
Abstract
The main part of optimal designs in the mixed effects models concentrates on linear models and binary models. Recently, Poisson models with random effects have been considered by some researchers. In this paper, an especial case of the mixed effects Poisson model, namely Poisson regression with random intercept is considered. Experimental design variations are obtained in terms of the random effect variance and indicated that the variations depend on the variance parameter. Using D-efficiency criterion, the impression of random effect on the experimental setting points is studied. These points are compared with the optimal experimental setting points in the corresponding model without random effect. We indicate that the D-efficiency depends on the variance of random effect.
Sahar Mehrmansour, Mehrdad Niaparast,
Volume 8, Issue 2 (3-2015)
Abstract
The main researches of optimum experimental designs for mixed effects have been concentrated on locally optimal designs. These designs are obtained based on the initial guess of parameters. Therefore, locally designs may be the best design but for wrong assumed model. Recently, Bayesian approach has been considered by researches when information about model parameters is available. In the present work, optimal design for the mixed effects Poisson regression model based on some prior distributions are considered and for two special cases of this models the Bayesian D-optimal designs are obtained for some representative values of variance of random effect. The results are compared to Poisson regression model without random effects.
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