Bayesian sparse convex clustering via global-local shrinkage priors

Sparse convex clustering is to group observations and conduct variable selection simultaneously in the framework of convex clustering. Although a weighted L 1 norm is usually employed for the regularization term in sparse convex clustering, its use increases the dependence on the data and reduces th...

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Bibliographic Details
Published inComputational statistics Vol. 36; no. 4; pp. 2671 - 2699
Main Authors Shimamura, Kaito, Kawano, Shuichi
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2021
Springer Nature B.V
Subjects
Algorithms
Bayesian analysis
Clustering
Data analysis
Economic Theory/Quantitative Economics/Mathematical Methods
Mathematics and Statistics
Original Paper
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Regularization
Shrinkage
Statistics
Horseshoe distribution
Normal–exponential–gamma distribution
Normal–gamma distribution
Dirichlet–Laplace distribution
Markov chain Monte Carlo
Hierarchical Bayesian model
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Summary:Sparse convex clustering is to group observations and conduct variable selection simultaneously in the framework of convex clustering. Although a weighted 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.
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-021-01101-7