Bayes inference in the Tobit censored regression model *
نویسنده
چکیده
We consider the Bayes estimation of the Tobit censored regression model with normally distributed errors. A simple condition for the existence of posterior moments is provided. Suitable versions of Monte Carlo procedures based on symmetric multivariate-t distributions, and Laplacian approximations in a certain parametrization, are developed and illustrated. Ideas involving data augmentation and Gibbs sampling [cf. Tanner and Wong (1987) and Gelfand and Smith (1990)] are also developed. The methods are compared in two examples with diffuse priors, and various combinations of sample sizes and degrees of censoring.
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