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2016 A nonparametric Bayesian technique for high-dimensional regression
Subharup Guha, Veerabhadran Baladandayuthapani
Electron. J. Statist. 10(2): 3374-3424 (2016). DOI: 10.1214/16-EJS1184

Abstract

This paper proposes a nonparametric Bayesian framework called VariScan for simultaneous clustering, variable selection, and prediction in high-throughput regression settings. Poisson-Dirichlet processes are utilized to detect lower-dimensional latent clusters of covariates. An adaptive nonlinear prediction model is constructed for the response, achieving a balance between model parsimony and flexibility. Contrary to conventional belief, cluster detection is shown to be a posteriori consistent for a general class of models as the number of covariates and subjects grows. Simulation studies and data analyses demonstrate that VariScan often outperforms several well-known statistical methods.

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Subharup Guha. Veerabhadran Baladandayuthapani. "A nonparametric Bayesian technique for high-dimensional regression." Electron. J. Statist. 10 (2) 3374 - 3424, 2016. https://doi.org/10.1214/16-EJS1184

Information

Received: 1 December 2015; Published: 2016
First available in Project Euclid: 16 November 2016

zbMATH: 1358.62059
MathSciNet: MR3572854
Digital Object Identifier: 10.1214/16-EJS1184

Keywords: Dirichlet process , local clustering , Model-based clustering , nonparametric Bayes , Poisson-Dirichlet process

Rights: Copyright © 2016 The Institute of Mathematical Statistics and the Bernoulli Society

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Vol.10 • No. 2 • 2016
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