A Nonparametric Bayesian Technique for High-Dimensional Regression [article]

Subharup Guha, Veerabhadran Baladandayuthapani
<span title="2016-04-12">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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 aposteriori consistent
more &raquo; ... 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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