# Bayesian Fusion Estimation via t-Shrinkage

## TL;DR

This paper introduces a Bayesian t-shrinkage prior for high-dimensional parameter modeling with unknown block structures, demonstrating superior performance over existing methods in simulations and real data analyses.

## Contribution

It proposes a novel t-fusion prior that induces stronger shrinkage and posterior consistency, extending Bayesian fusion and clustering techniques.

## Key findings

- t-fusion outperforms Bayesian Laplace-fusion in simulations and real data.
- The method achieves better posterior convergence than Dirichlet process models.
- t-fusion effectively induces posterior blocking in high-dimensional parameters.

## 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 simulation shows that the proposed algorithm obtains better posterior distributional convergence than the classical Dirichlet process modeling.