A general framework for multiple testing dependence. Export

@article{citeulike:3687582, abstract = {We develop a general framework for performing large-scale significance testing in the presence of arbitrarily strong dependence. We derive a low-dimensional set of random vectors, called a dependence kernel, that fully captures the dependence structure in an observed high-dimensional dataset. This result shows a surprising reversal of the "curse of dimensionality" in the high-dimensional hypothesis testing setting. We show theoretically that conditioning on a dependence kernel is sufficient to render statistical tests independent regardless of the level of dependence in the observed data. This framework for multiple testing dependence has implications in a variety of common multiple testing problems, such as in gene expression studies, brain imaging, and spatial epidemiology.}, author = {Leek, Jeffrey T. and Storey, John D.}, citeulike-article-id = {3687582}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0808709105}, citeulike-linkout-1 = {http://www.pnas.org/content/0808709105v1//.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/0808709105v1//.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19033188}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19033188}, day = {2}, doi = {10.1073/pnas.0808709105}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {dependence, multiple, testing}, month = {December}, number = {48}, pages = {18718--18723}, posted-at = {2008-12-03 09:52:53}, priority = {2}, title = {A general framework for multiple testing dependence.}, url = {http://dx.doi.org/10.1073/pnas.0808709105}, volume = {105}, year = {2008} }

TY - JOUR ID - citeulike:3687582 L3 - citeulike-article-id:3687582 N2 - We develop a general framework for performing large-scale significance testing in the presence of arbitrarily strong dependence. We derive a low-dimensional set of random vectors, called a dependence kernel, that fully captures the dependence structure in an observed high-dimensional dataset. This result shows a surprising reversal of the "curse of dimensionality" in the high-dimensional hypothesis testing setting. We show theoretically that conditioning on a dependence kernel is sufficient to render statistical tests independent regardless of the level of dependence in the observed data. This framework for multiple testing dependence has implications in a variety of common multiple testing problems, such as in gene expression studies, brain imaging, and spatial epidemiology. IS - 48 JF - Proceedings of the National Academy of Sciences of the United States of America SN - 1091-6490 EP - 18723 TI - A general framework for multiple testing dependence. VL - 105 SP - 18718 KW - dependence KW - multiple KW - testing AU - Leek, Jeffrey AU - Storey, John PY - 2008/12/02/ UR - http://dx.doi.org/10.1073/pnas.0808709105 ER -