Robust estimation of the false discovery rate | Zendy

Stanley Pounds | Zendy; Cheng Cheng | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Robust estimation of the false discovery rate

Author(s) -

Stanley Pounds,

Cheng Cheng

Publication year - 2006

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btl328

Subject(s) - false discovery rate , computer science , multiple comparisons problem , simple (philosophy) , series (stratigraphy) , algorithm , estimation , data mining , mathematics , statistics , paleontology , biochemistry , chemistry , philosophy , management , epistemology , biology , economics , gene

Presently available methods that use p-values to estimate or control the false discovery rate (FDR) implicitly assume that p-values are continuously distributed and based on two-sided tests. Therefore, it is difficult to reliably estimate the FDR when p-values are discrete or based on one-sided tests.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research