Bayes inference in the Tobit censored regression model. (English) Zbl 0742.62033
Summary: 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 are also developed. The methods are compared in two examples with diffuse priors, and various combinations of sample sizes and degrees of censoring.
MSC:
| 62F15 | Bayesian inference |
| 62J05 | Linear regression; mixed models |
| 62P20 | Applications of statistics to economics |
| 65C99 | Probabilistic methods, stochastic differential equations |
Keywords:
Bayes estimation; Tobit censored regression model; normally distributed errors; existence of posterior moments; Monte Carlo procedures; symmetric multivariate-\(t\) distributions; Laplacian approximations; data augmentation; Gibbs sampling; diffuse priors; censoringReferences:
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