Accurate and Efficient P-value Calculation via Gaussian Approximation: a Novel Monte-Carlo Method.

ABSTRACT: It is of fundamental interest in statistics to test the significance of a set of covariates. For example, in genome-wide association studies, a joint null hypothesis of no genetic effect is tested for a set of multiple genetic variants. The minimum p-value method, higher criticism, and Berk-Jones tests are particularly effective when the covariates with nonzero effects are sparse. However, the correlations among covariates and the non-Gaussian distribution of the response pose a great challenge towards the p-value calculation of the three tests. In practice, permutation is commonly used to obtain accurate p-values, but it is computationally very intensive, especially when we need to conduct a large amount of hypothesis testing. In this paper, we propose a Gaussian appr

SUBMITTER: Liu Y 

PROVIDER: S-EPMC6530914 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature