Bayesian nonparametric inference for unimodal skew-symmetric distributions

This paper studies the case where the observations come from a unimodal and skew density function with an unknown mode. The skew-symmetric representation of such a density has a symmetric component which can be written as a scale mixture of uniform densities. A Dirichlet process (DP) prior is assigned to mixing distribution. We also assume prior distributions for the mode and the skewed component. A computational approach is used to obtain the Bayes estimate of the components. An example is given to illustrate the approach.