Bayesian calculus for gamma processes with applications to semiparametric intensity models
Lancelot F. James · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2003
Explicit calculus for the posterior distribution of convolution mixtures of weighted gamma processes on Polish spaces are derived. This serves to extend the results of Lo and Weng (1989) to a semiparametric setting on arbitrary spaces. The result of this study is applied to two different types of general semiparametric multiplicative intensity models. One in which a prior is constructed based on q conditionally independent weighted gamma measures given a Euclidean parameter and a second dependent model where different hazard rates are based on a common mixing measure. The latter model seems natural for some types of deconvolution models or regression models. As an example, it is shown how this provides a full (implementable) posterior analysis of the Cox regression model. The results also provide the explicit posterior distribution for the Poisson/Gamma random field model considered by Wolpert and Ickstadt (1998a).