Bayesian Fusion Estimation via t Shrinkage

Qifan Song () and Guang Cheng ()
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Qifan Song: Purdue University
Guang Cheng: Purdue University

Sankhya A: The Indian Journal of Statistics, 2020, vol. 82, issue 2, No 4, 353-385

Abstract: Abstract Shrinkage prior has gained great successes in many data analysis, however, its applications mostly focus on the Bayesian modeling of sparse parameters. In this work, we will apply Bayesian shrinkage to model high dimensional parameter that possesses an unknown blocking structure. We propose to impose heavy-tail shrinkage prior, e.g., t prior, on the differences of successive parameter entries, and such a fusion prior will shrink successive differences towards zero and hence induce posterior blocking. Comparing to conventional Bayesian fused LASSO which implements Laplace fusion prior, t fusion prior induces stronger shrinkage effect and enjoys a nice posterior consistency property. Simulation studies and real data analyses show that t fusion has superior performance to the frequentist fusion estimator and Bayesian Laplace fusion prior. This t fusion strategy is further developed to conduct a Bayesian clustering analysis, and our simulations show that the proposed algorithm compares favorably to classical Dirichlet process modeling.

Keywords: t shrinkage prior; Bayesian fusion; Bayesian clustering; Posterior consistency; Primary 62F15; Secondary 62J07 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13171-019-00177-0

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