Bayesian Analysis of Semiparametric Proportional Hazards Models
STANFORD UNIV CA DEPT OF STATISTICS
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We consider the usual proportional hazards model in the case where the baseline hazard, the covariate link and the covariate coefficients are all unknown. Both the baseline hazard and the covariate link are monotone functions and are characterized nonparametrically using a dense class arising as a mixture of Beta distribution functions. We take a Bayesian approach for fitting such a model. Since interest focuses more upon the likelihood, we consider vague prior specifications including Jeffreyss prior. Computations are carried out using sampling-based methods. Model criticism is also discussed. Finally, a data set studying survival of a sample of lung cancer patients is analyzed.
- Statistics and Probability