Bayesian sparse convex clustering via global-local shrinkage priors

Kaito Shimamura () and Shuichi Kawano ()
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Kaito Shimamura: NTT Advanced Technology Corporation
Shuichi Kawano: The University of Electro-Communications

Computational Statistics, 2021, vol. 36, issue 4, No 14, 2699 pages

Abstract: Abstract Sparse convex clustering is to group observations and conduct variable selection simultaneously in the framework of convex clustering. Although a weighted $$L_1$$ L 1 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.

Keywords: Dirichlet–Laplace distribution; Hierarchical Bayesian model; Horseshoe distribution; Normal–gamma distribution; Normal–exponential–gamma distribution; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00180-021-01101-7

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