activity
20122024
most citedCoordinate-independent sparse sufficient dimension reduction and variable selection

144 citations · 147 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ME2024

Conditional Testing based on Localized Conformal p-values

Xiaoyang Wu, Lin Lu, Zhaojun Wang +1

In this paper, we address conditional testing problems through the conformal inference framework. We define the localized conformal p-values by inverting prediction intervals and p…

stat.ME2022

Model-Free Statistical Inference on High-Dimensional Data

Xu Guo, Runze Li, Zhe Zhang +1

This paper aims to develop an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduc…

stat.ME20211 cited

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…

math.ST20202 cited

A New Procedure for Controlling False Discovery Rate in Large-Scale t-tests

Changliang Zou, Haojie Ren, Xu Guo +1

This paper is concerned with false discovery rate (FDR) control in large-scale multiple testing problems. We first propose a new data-driven testing procedure for controlling the F…

math.ST2012144 cited

Coordinate-independent sparse sufficient dimension reduction and variable selection

Xin Chen, Changliang Zou, R. Dennis Cook

Sufficient dimension reduction (SDR) in regression, which reduces the dimension by replacing original predictors with a minimal set of their linear combinations without loss of inf…