title: A decision-theoretical alternative to testing many hypotheses.
abstract: Testing a large number of hypotheses is a key problem in the analysis of microarray experiments and in other studies in which many elementary experiments are conducted, and the exceptions among them, for which a particular hypothesis does not hold, have to be identified. A class of approaches to this problem is based on controlling the false discovery rate, even though failure to discover should also be considered. We develop a decision-theoretical approach in which errors (misclassifications) of the two kinds are associated with uneven losses, and the total expected loss in the collection of the classifications (decisions made or options selected) is minimized.
authors.affiliation: SNTL and Universitat Pompeu Fabra, Ramon Trias Fargas 25-27, 08005 Barcelona, Spain.
authors.forename: Nicholas T
authors.initials: NT
authors.lastname: Longford
mesh_terms: D001499:Bayes Theorem; D003110:Colonic Neoplasms / Q000235:genetics; D003198:Computer Simulation; D003662:Decision Theory; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods
keywords: Classification; Decision theory; Expected loss; False discovery rate; Hypothesis testing; Penalty ratio