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Bayesian Analysisof Multivariate Probit Modelswith Surrogate Outcome Dataonline by Yanis Remmache is a document available to read on EtoBox.

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This document presents a new class of parametric models that generalize the multivariate probit model to analyze ordinal data using surrogate outcome data. It introduces a Bayesian framework and a hybrid Gibbs sampler for estimating model parameters, addressing issues of misclassification in surrogate variables. The proposed methodology is illustrated with real data examples and simulation studies, demonstrating its applicability and effectiveness in statistical analysis.

Author

Yanis Remmache

Language

EN