Bayesian sparse convex clustering via global-local shrinkage priors

Kaito Shimamura, Shuichi Kawano

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Sparse convex clustering is to group observations and conduct variable selection simultaneously in the framework of convex clustering. Although a weighted L1 norm is usually employed for the regularization term in sparse convex clustering, its use increases the dependence on the data and reduces the estimation accuracy if the sample size is not sufficient. To tackle these problems, this paper proposes a Bayesian sparse convex clustering method based on the ideas of Bayesian lasso and global-local shrinkage priors. We introduce Gibbs sampling algorithms for our method using scale mixtures of normal distributions. The effectiveness of the proposed methods is shown in simulation studies and a real data analysis.

Original languageEnglish
Pages (from-to)2671-2699
Number of pages29
JournalComputational Statistics
Volume36
Issue number4
DOIs
Publication statusPublished - Dec 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Mathematics

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