A general framework for multiple testing dependence.
Proc Natl Acad Sci U S A
; 105(48): 18718-23, 2008 Dec 02.
Article
em En
| MEDLINE
| ID: mdl-19033188
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Modelos Estatísticos
Tipo de estudo:
Risk_factors_studies
Idioma:
En
Revista:
Proc Natl Acad Sci U S A
Ano de publicação:
2008
Tipo de documento:
Article
País de afiliação:
Estados Unidos