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Bayesian estimation of a random effects heteroscedastic probit model. (English) Zbl 1206.62038

Summary: Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross-validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison.

MSC:

62F15 Bayesian inference
65C40 Numerical analysis or methods applied to Markov chains
62P20 Applications of statistics to economics
62F10 Point estimation
65C60 Computational problems in statistics (MSC2010)
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