26 citations · 35 across the 6 of their papers we have counts for
6 papers
Multiple Testing under High-dimensional Dynamic Factor Model
Xinxin Yang, Lilun Du
Large-scale multiple testing under static factor models is widely used to detect sparse signals in high-dimensional data. However, static factor models are arguably too stringent b…
Dynamic statistical inference in massive datastreams
Jingshen Wang, Lilun Du, Changliang Zou +1
Modern technological advances have expanded the scope of applications requiring analysis of large-scale datastreams that comprise multiple indefinitely long time series. There is a…
False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation
Lilun Du, Xu Guo, Wenguang Sun +1
We develop a new class of distribution--free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized…
An Empirical Bayes Method for Chi-Squared Data
Lilun Du, Inchi Hu
In a thought-provoking paper, Efron (2011) investigated the merit and limitation of an empirical Bayes method to correct selection bias based on Tweedie's formula first reported by…
A Factor-Adjusted Multiple Testing Procedure with Application to Mutual Fund Selection
Wei Lan, Lilun Du
In this article, we propose a factor-adjusted multiple testing (FAT) procedure based on factor-adjusted p-values in a linear factor model involving some observable and unobservable…
Single-index modulated multiple testing
Lilun Du, Chunming Zhang
In the context of large-scale multiple testing, hypotheses are often accompanied with certain prior information. In this paper, we present a single-index modulated (SIM) multiple t…