Nonparametric Bayes Inference for Concave Distribution Functions
نویسنده
چکیده
A way of making Bayesian inference for concave distribution functions is introduced. This is done by uniquely transforming a mixture of Dirichlet processes on the space of distribution functions to the space of concave distribution functions. The approach also gives a way of making Bayesian analysis of mul-tiplicatively censored data. We give a method for sampling from the posterior distribution by use of a PP olya urn scheme in combination with a Markov chain Monte Carlo algorithm. The methods are extended to estimation of concave distribution functions for incompletely observed data. Finally, consistency issues are touched upon.
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