Abstract
We consider the problem of variable selection in data sets with many response variables and many covariates. A method is proposed that allows some covariates to affect some response variables and not others, and that clusters responses which have similar dependence on the same set of covariates. A Markov chain Monte Carlo procedure is employed to sample from the space of pairwise partitions of covariates and outcomes, where a pair consists of a subset of all outcomes and their associated covariates. We assess the performance of the method on simulated data and apply it to genomic data.
Citation
Stefano Monni. Mahlet G. Tadesse. "A stochastic partitioning method to associate high-dimensional responses and covariates." Bayesian Anal. 4 (3) 413 - 436, 2009. https://doi.org/10.1214/09-BA416
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