Nonparametric Bayes estimators based on beta processes in models for life history data
The problem of finding Bayes estimators for cumulative hazard rates and related quantities, w.r.t. prior distributions that correspond to cumulative hazard rate processes with positive, independent increments, is studied. A class of prior processes, termed beta processes, is introduced and shown to constitute a conjugate class. As an introduction, a nonparametric time-discrete model for survival data, which is of some independent interest, is studied. An advantage of modelling cumulative hazard rates instead of cumulative distribution functions is that extensions are possible, for instance to time-inhomogeneous Markov chains.
semiparametric Bayesian analysis of the Cox regression model
scientific article; zbMATH DE number 4169868