Posterior Computations for Censored Regression Data

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 posteri...

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Bibliographic Details
Published in:Journal of the American Statistical Association 1990-09, Vol.85 (411), p.829-839
Main Authors: Wei, Greg C. G., Tanner, Martin A.
Format: Article
Language:English
Subjects:
Approximation
Bayesian inference
Censored data
Data augmentation
Datasets
Density estimation
Exact sciences and technology
Gaussian distributions
Histograms
Linear inference, regression
Mathematics
Multiple imputation
Parametric models
Probability and statistics
Regression analysis
Sciences and techniques of general use
Simulation
Standard deviation
Statistics
Tanneries
Theory and Methods
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Summary: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.
ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.1990.10474947