Computational Statistics & Data Analysis - First special issue on statistical modelling
A Monte Carlo EM method for estimating multinomial probit models
Computational Statistics & Data Analysis
WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility
Statistics and Computing
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
A note on the estimation of the multinomial logistic model with correlated responses in SAS
Computer Methods and Programs in Biomedicine
Categorical data analysis using the sas® system, 2nd edition
Categorical data analysis using the sas® system, 2nd edition
Estimation in the probit normal model for binary outcomes using the SAEM algorithm
Computational Statistics & Data Analysis
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Analysis of discrete repeated outcomes is an important issue in biomedical studies. The aim of this paper is to propose a flexible and parsimonious model to account for heterogeneous variances for discrete outcomes. The proposed method is based on the use of a linear mixed model on the log of the standard deviation parameters. It is also shown how parameter estimation in this model can be performed with an exact procedure based on a Gibbs sampling algorithm implemented with the Winbugs/Openbugs software. A model comparison study is presented to illustrate the efficiency of this procedure using a well known example from the clinical trial literature. It was found that the proposed methodology considerably improved the predictive ability of the model while remaining very parsimonious. In particular, it was found that adding a random subject effect in the variance model significantly improved the posterior predictive p-value criterion of the model.