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A Nonparametric Bayesian Technique for High-Dimensional Regression
[article]
2016
arXiv
pre-print
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
arXiv:1604.03615v1
fatcat:ve3qbix3ujfetfqfg6cfsphnuq