Nonparametric Bayes inference for concave distribution functions

Martin B. Hansen and Steffen L. Lauritzen

Statistica Neerlandica, 2002, vol. 56, issue 1, 110-127

Abstract: Bayesian inference for concave distribution functions is investigated. This is made by transforming a mixture of Dirichlet processes on the space of distribution functions to the space of concave distribution functions. We give a method for sampling from the posterior distribution using a Pólya 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.

Date: 2002
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