activity
20182021
most citedA New Procedure for Controlling False Discovery Rate in Large-Scale t-tests

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

stat.ME2021

Statistical Inference for Linear Mediation Models with High-dimensional Mediators and Application to Studying Stock Reaction to COVID-19 Pandemic

Xu Guo, Runze Li, Jingyuan Liu +1

Mediation analysis draws increasing attention in many scientific areas such as genomics, epidemiology and finance. In this paper, we propose new statistical inference procedures fo…

math.ST20201 cited

A projection-based model checking for heterogeneous treatment effect

Niwen Zhou, Xu Guo, Lixing Zhu

In this paper, we investigate the hypothesis testing problem that checks whether part of covariates / confounders significantly affect the heterogeneous treatment effect given all…

math.ST2020

The Role of Propensity Score Structure in Asymptotic Efficiency of Estimated Conditional Quantile Treatment Effect

Niwen Zhou, Xu Guo, Lixing Zhu

When a strict subset of covariates are given, we propose conditional quantile treatment effect to capture the heterogeneity of treatment effects via the quantile sheet that is the…

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…

stat.ME2018

Testing heteroscedasticity for regression models based on projections

Falong Tan, Xuejun Jiang, Xu Guo +1

In this paper we propose a new test of heteroscedasticity for parametric regression models and partial linear regression models in high dimensional settings. When the dimension of…