Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

2018

Journal or Book Title

The Annals of Statistics

Volume

46

Issue

2

First Page

895

Last Page

923

DOI

10.1214/17-AOS1574

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.

Comments

This article is published as Qiu, Yumou, Song Xi Chen, and Dan Nettleton. "Detecting rare and faint signals via thresholding maximum likelihood estimators." The Annals of Statistics 46, no. 2 (2018): 895-923. doi: 10.1214/17-AOS1574. Posted with permission.

Copyright Owner

Institute of Mathematical Statistics

Language

en

File Format

application/pdf

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