A decision-theoretical alternative to testing many hypotheses
N.T. Longford
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 minimised.
Biostatistics 15, 164-179, 2014.
January 2014.