This chapter compares two Bayesian nonparametric models that generalize the accelerated failure time model, based on recent work on probability models for predictor-dependent probability distributions. It begins by reviewing ...

We discuss Bayesian nonparametric procedures for the regression analysis of compositional responses, that is, data supported on a multivariate simplex. The procedures are based on a modified class of multivariate Bernstein ...

Standard regression approaches assume that some finite number of the response distribution characteristics, such as location and scale, change as a (parametric or nonparametric) function of predictors. However, it is not ...

We propose a novel class of probability models for sets of predictor-dependent probability distributions with bounded domain. The proposal extends the DirichletBernstein prior for single density estimation, by using dependent ...

We propose a novel class of probability models for sets of predictor-dependent probability distributions with bounded domain. The proposal extends the DirichletBernstein prior for single density estimation, by using dependent ...

We propose a fully nonparametric modelling approach for time-to-event regression data, when the response of interest can only be determined to lie in an interval obtained from a sequence of examination times and the ...

We propose a fully nonparametric modelling approach for time-to-event regression data, when the response of interest can only be determined to lie in an interval obtained from a sequence of examination times and the ...