Making the cut: improved ranking and selection for large-scale inference.

ABSTRACT: Identifying leading measurement units from a large collection is a common inference task in various domains of large-scale inference. Testing approaches, which measure evidence against a null hypothesis rather than effect magnitude, tend to overpopulate lists of leading units with those associated with low measurement error. By contrast, local maximum likelihood (ML) approaches tend to favor units with high measurement error. Available Bayesian and empirical Bayesian approaches rely on specialized loss functions that result in similar deficiencies. We describe and evaluate a generic empirical Bayesian ranking procedure that populates the list of top units in a way that maximizes the expected overlap between the true and reported top lists for all list sizes. The procedure relates unit-spec

SUBMITTER: Henderson NC 

PROVIDER: S-EPMC4996506 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature