Learning from a lot: Empirical Bayes for high-dimensional model-based prediction.

ABSTRACT: Empirical Bayes is a versatile approach to "learn from a lot" in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well-known model-based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss "formal" empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross-validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables,

SUBMITTER: van de Wiel MA 

PROVIDER: S-EPMC6472625 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature