Detecting rare and faint signals via thresholding maximum likelihood estimators

7Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.

Abstract

Motivated by the analysis of RNA sequencing (RNA-seq) data for genes differentially expressed across multiple conditions, we consider detecting rare and faint signals in high-dimensional response variables. We address the signal detection problem under a general framework, which includes generalized linear models for count-valued responses as special cases. We propose a test statistic that carries out a multi-level thresholding on maximum likelihood estimators (MLEs) of the signals, based on a new Cramér-type moderate deviation result for multidimensional MLEs. Based on the multi-level thresholding test, a multiple testing procedure is proposed for signal identification. Numerical simulations and a case study on maize RNA-seq data are conducted to demonstrate the effectiveness of the proposed approaches on signal detection and identification.

Cite

CITATION STYLE

APA

Qiu, Y., Chen, S. X., & Nettleton, D. (2018). Detecting rare and faint signals via thresholding maximum likelihood estimators. Annals of Statistics, 46(2), 895–923. https://doi.org/10.1214/17-AOS1574

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free