Data-adaptive Shrinkage via the Hyperpenalized EM Algorithm.

ABSTRACT: We propose an extension of the expectation-maximization (EM) algorithm, called the hyperpenalized EM (HEM) algorithm, that maximizes a penalized log-likelihood, for which some data are missing or unavailable, using a data-adaptive estimate of the penalty parameter. This is potentially useful in applications for which the analyst is unable or unwilling to choose a single value of a penalty parameter but instead can posit a plausible range of values. The HEM algorithm is conceptually straightforward and also very effective, and we demonstrate its utility in the analysis of a genomic data set. Gene expression measurements and clinical covariates were used to predict survival time. However, many survival times are censored, and some observations only contain expression measurements derived fro

SUBMITTER: Boonstra PS 

PROVIDER: S-EPMC4728141 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature