Bayesian sparse convex clustering via global-local shrinkage priors

• Published in: Computational statistics Vol. 36; no. 4; pp. 2671 - 2699
• Main Authors: Shimamura, Kaito, Kawano, Shuichi
• Format: Journal Article
• Language: English
• 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

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...