title: 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 from a different assay, which together constitute a difficult missing data problem. It is desired to shrink the genomic contribution in a data-adaptive way. The HEM algorithm successfully handles both the missing data and shrinkage aspects of the problem.
authors.affiliation: Department of Biostatistics, University of Michigan, 1415 Washington Hts., Ann Arbor, MI, USA. Tel. +1 (734) 615-1580.
authors.forename: Philip S
authors.initials: PS
authors.lastname: Boonstra
authors.affiliation: Department of Biostatistics, University of Michigan, 1415 Washington Hts., Ann Arbor, MI, USA. Tel. +1 (734) 615-1580.
authors.forename: Jeremy M G
authors.initials: JM
authors.lastname: Taylor
authors.affiliation: Department of Biostatistics, University of Michigan, 1415 Washington Hts., Ann Arbor, MI, USA. Tel. +1 (734) 615-1580.
authors.forename: Bhramar
authors.initials: B
authors.lastname: Mukherjee
keywords: EM algorithm; hyperparameter; hyperpenalty; missing data; penalized likelihood; prediction
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