BAYESIAN SHRINKAGE METHODS FOR PARTIALLY OBSERVED DATA WITH MANY PREDICTORS.

ABSTRACT: Motivated by the increasing use of and rapid changes in array technologies, we consider the prediction problem of fitting a linear regression relating a continuous outcome Y to a large number of covariates X , eg measurements from current, state-of-the-art technology. For most of the samples, only the outcome Y and surrogate covariates, W , are available. These surrogates may be data from prior studies using older technologies. Owing to the dimension of the problem and the large fraction of missing information, a critical issue is appropriate shrinkage of model parameters for an optimal bias-variance tradeoff. We discuss a variety of fully Bayesian and Empirical Bayes algorithms which account for uncertainty in the missing data and adaptively shrin

SUBMITTER: Boonstra PS 

PROVIDER: S-EPMC3891514 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature