Nonparametric Estimator of False Discovery Rate Based on Bernšteǐn Polynomials

Loading...
Thumbnail Image
Can’t use the file because of accessibility barriers? Contact us with the title of the item, permanent link, and specifics of your accommodation need.

Date

2008

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Under a local dependence assumption about the p-values, an estimator of the proportion π0 of true null hypotheses, having a closed-form expression, is derived based on Bernšteǐn polynomial density estimation. A nonparametric estimator of false discovery rate (FDR) is then obtained. These estimators are proved to be consistent, asymptotically unbiased, and normal. Confidence intervals for π0 and the FDR are also given. The usefulness of the proposed method is demonstrated through simulations and its application to a microarray dataset. Keywords: Bernsteın polynomials, bioinformatics, density estimation, false discovery rate, local dependence, microarray, mixture model, multiple comparison

Description

Keywords

Mathematical statistics, Nonparametric statistics

Citation

Guan, Zhong, et al. “Nonparametric Estimator of False Discovery Rate Based on Bernšteǐn Polynomials.” Statistica Sinica, vol. 18, 2008, pp. 905–23.

Journal

DOI

Link(s) to data and video for this item

Relation

Rights

Type

Article