BAYESIAN SEMIPARAMETRIC ESTIMATION OF PROPORTIONAL HAZARDS MODELS

This paper proposes a semiparametric analysis of proportional hazards models. This approach consists in specifying the relation between a du ration and explanatory variables, without specifying the data distribu tion. The parameters involved in this relation are then considered as parameters of interest, and the data distribution is treated as a nuis ance parameter. We propose a Bayesian estimation method, the principle of which is to specify a prior distribution on the nuisance parameter . We then obtain semiparametric estimators for the parameters of inter est, by computing their posterior distribution, conditional on the dat a and integrated with respect to the nuisance parameter.