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Activity Number:
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312
- Bayesian Variable Selection: When Horseshoe Meets Nonlocal
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #312817
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Title:
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Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-Dimensional Model Selection
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Author(s):
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Minsuk Shin* and Anirban Battacharya
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Companies:
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University of South Carolina and Texas A&M University
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Keywords:
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Bayesian variable selection;
High-dimensional inference;
Nonparametric regression
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Abstract:
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In this talk, we introduce a new class of prior densities that can be used for nonparametric Bayesian hypothesis testing and model selection. Members of this class of prior densities are called non-local functional prior (NLfP) densities. We demonstrate that NLfP densities provide improved convergence rates in many nonparametric Bayesian hypothesis tests. When used to perform high-dimensional Bayesian model selection for nonparametric additive models, we show that the resulting procedures are consistent, and find that the model selection procedures based on NLfP densities outperform existing state-of-the-art alternatives in several standard simulation scenarios and real data sets.
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Authors who are presenting talks have a * after their name.
