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Activity Number: 355 - Advanced Bayesian Topics (Part 4)
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #318982
Title: A Characterization of the Likelihood Ratio Order in Terms of Mixtures
Author(s): Michael Jauch*
Companies: Cornell University
Keywords: likelihood ratio order; density estimation; hypothesis testing; stochastic order; nonparametric; Bayesian

The likelihood ratio plays an important role in statistical theory and also in practice. In applied contexts, it is often assumed that a pair of probability distributions satisfies a likelihood ratio order. Statistical inference is challenging in this setting due to the likelihood ratio constraint. To address this problem, we present a novel characterization of the likelihood ratio order in terms of mixtures which allows for unconstrained inference. We illustrate its utility in Bayesian nonparametric density estimation and hypothesis testing.

Authors who are presenting talks have a * after their name.

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