Posterior computations for censored regression data

Greg C G Wei, Martin A. Tanner

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)829-839
Number of pages11
JournalJournal of the American Statistical Association
Volume85
Issue number411
DOIs
StatePublished - Sep 1990

Keywords

  • Bayesian inference
  • Data augmentation
  • Multiple imputation
  • Simulation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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