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Activity Number: 36
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #320293
Title: On Likelihood Ratio Tests in Dimensionality-Restricted Models
Author(s): Mingyue Gao* and Michael Trosset and Carey Priebe
Companies: and Indiana University and The Johns Hopkins University
Keywords: likelihood ratio test ; multinomial ; dimensionality-restricted ; submodel ; power superiority

We study likelihood ratio tests (LRTs) in submodels of multinomial models with simple null and general alternative hypotheses. If the dimension of the submodel is less than the dimension of the full model, then the restricted LRT is asymptotically more powerful against local alternatives than the unrestricted LRT. However, for every non-trivial dimensionality-restricted submodel, for any finite sample size, there exists simple null and alternative hypotheses, and a significance level for which the restricted LRT is less powerful than the unrestricted LRT.

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

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