  The mean residual life function is a key functional for a survival
distribution. It has a practically useful interpretation as the expected
remaining lifetime given survival up to a particular time point, and it also
characterizes the survival distribution. However, it has received limited
attention in terms of inference methods under a probabilistic modeling
framework. We seek to provide general inference methodology for mean residual
life regression. We employ Dirichlet process mixture modeling for the joint
stochastic mechanism of the covariates and the survival response. This density
regression approach implies a flexible model structure for the mean residual
life of the conditional response distribution, allowing general shapes for mean
residual life as a function of covariates given a specific time point, as well
as a function of time given particular values of the covariates. We further
extend the mixture model to incorporate dependence across experimental groups.
This extension is built from a dependent Dirichlet process prior for the
group-specific mixing distributions, with common atoms and weights that vary
across groups through latent bivariate Beta distributed random variables. We
discuss properties of the regression models, and develop methods for posterior
inference. The different components of the methodology are illustrated with
simulated data examples, and the model is also applied to a data set comprising
right censored survival times.
