  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 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.
