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Bayesian sparse convex clustering via global-local shrinkage priors

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  • Kaito Shimamura

    (NTT Advanced Technology Corporation
    The University of Electro-Communications)

  • Shuichi Kawano

    (The University of Electro-Communications)

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.

Suggested Citation

  • Kaito Shimamura & Shuichi Kawano, 2021. "Bayesian sparse convex clustering via global-local shrinkage priors," Computational Statistics, Springer, vol. 36(4), pages 2671-2699, December.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:4:d:10.1007_s00180-021-01101-7
    DOI: 10.1007/s00180-021-01101-7
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    References listed on IDEAS

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