Bayesian Analysis

Compound Poisson Processes, Latent Shrinkage Priors and Bayesian Nonconvex Penalization

Zhihua Zhang and Jin Li

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In this paper we discuss Bayesian nonconvex penalization for sparse learning problems. We explore a nonparametric formulation for latent shrinkage parameters using subordinators which are one-dimensional Lévy processes. We particularly study a family of continuous compound Poisson subordinators and a family of discrete compound Poisson subordinators. We exemplify four specific subordinators: Gamma, Poisson, negative binomial and squared Bessel subordinators. The Laplace exponents of the subordinators are Bernstein functions, so they can be used as sparsity-inducing nonconvex penalty functions. We exploit these subordinators in regression problems, yielding a hierarchical model with multiple regularization parameters. We devise ECME (Expectation/Conditional Maximization Either) algorithms to simultaneously estimate regression coefficients and regularization parameters. The empirical evaluation of simulated data shows that our approach is feasible and effective in high-dimensional data analysis.

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Bayesian Anal. Volume 10, Number 2 (2015), 247-274.

First available in Project Euclid: 2 February 2015

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nonconvex penalization subordinators latent shrinkage parameters Bernstein functions ECME algorithms


Zhang, Zhihua; Li, Jin. Compound Poisson Processes, Latent Shrinkage Priors and Bayesian Nonconvex Penalization. Bayesian Anal. 10 (2015), no. 2, 247--274. doi:10.1214/14-BA892.

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