Posterior Computations for Censored Regression Data

Abstract This article describes the computation of and sampling from the posterior density for censored regression problems with normal and generalized log-gamma errors. The data augmentation algorithm (Tanner and Wong 1987) is facilitated in the normal error case because of the form of the augmented posterior. In the generalized log-gamma context, this simplicity is absent. The work of Sweeting (1981) is used as a motivation to develop an importance sampling scheme to sample from an augmented posterior. It is shown how the predictive distribution for a new observation may be computed and sampled from. The methodology is illustrated with two examples.

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