Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Exploring The Information In P-Values For The Analysis And Planning Of Multiple-Test Experiments, David Ruppert, Dan Nettleton, J.T. Gene Hwang
Exploring The Information In P-Values For The Analysis And Planning Of Multiple-Test Experiments, David Ruppert, Dan Nettleton, J.T. Gene Hwang
Dan Nettleton
A new methodology is proposed for estimating the proportion of true null hypotheses in a large collection of tests. Each test concerns a single parameter δ whose value is specified by the null hypothesis. We combines a parametric model for the conditional CDF of the p-value given δ with a nonparametric spline model for the density g(δ) of δ under the alternative hypothesis. The proportion of true null hypotheses and the coefficients in the spline model are estimated by penalized least-squares subject to constraints that guarantee that the spline is a density. The estimator is computed efficiently using quadratic programming. …
A Hidden Markov Model Approach To Testing Multiple Hypotheses On A Gene Ontology Graph, Kun Liang, Dan Nettleton
A Hidden Markov Model Approach To Testing Multiple Hypotheses On A Gene Ontology Graph, Kun Liang, Dan Nettleton
Dan Nettleton
Gene category testing problems involve testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The logical relationships among the nodes in the graph imply that only some configurations of true and false null hypotheses are possible and that a test for a given node should depend on data from neighboring nodes. We developed a method based on a hidden Markov model that takes the whole graph into account and provides coherent decisions in this structured multiple hypothesis testing problem. The method is illustrated by testing Gene Ontology terms for evidence of differential expression.