Title: Mixture representations for likelihood ratio ordered distributions
Authors: Michael Jauch - Florida State University (United States)
Andres Barrientos - Florida State University (United States) [presenting]
Victor Pena - Universitat Politecnica de Catalunya (Spain)
David Matteson - Cornell University (United States)
Abstract: Mixture representations for likelihood ratio ordered distributions are introduced. Essentially, the ratio of two probability densities, or mass functions, is monotone if and only if one can be expressed as a mixture of one-sided truncations of the other. To illustrate the practical value of the mixture representations, we address the problem of density estimation for likelihood ratio ordered distributions. In particular, we propose a nonparametric Bayesian solution which takes advantage of the mixture representations. The prior distribution is constructed from Dirichlet process mixtures and has large support on the space of pairs of densities satisfying the monotone ratio constraint. With a simple modification to the prior distribution, we can test the equality of two distributions against the alternative of likelihood ratio ordering. We develop a Markov chain Monte Carlo algorithm for posterior inference and demonstrate the method in a biomedical application.