6 citations · 20 across the 12 of their papers we have counts for
12 papers
Weighted residual empirical processes, martingale transformations, and model specification tests for regressions with diverging number of parameters
Falong Tan, Xu Guo, Lixing Zhu
This paper explores hypothesis testing for the parametric forms of the mean and variance functions in regression models under diverging-dimension settings. To mitigate the curse of…
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…
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…
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…
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…
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…
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…
An adaptive-to-model test for partially parametric single-index models
Xuehu Zhu, Xu Guo, Lixing Zhu
Residual marked empirical process-based tests are commonly used in regression models. However, they suffer from data sparseness in high-dimensional space when there are many covari…
Heteroscedasticity Testing for Regression Models: A Dimension Reduction-based Model Adaptive
Xuehu Zhu, Fei Chen, Xu Guo +1
Heteroscedasticity testing is of importance in regression analysis. Existing local smoothing tests suffer severely from curse of dimensionality even when the number of covariates i…
Enhancements of nonparametric generalized likelihood ratio test: Bias-correction and dimension reduction
Cuizhen Niu, Xu Guo, Lixing Zhu
Nonparametric generalized likelihood ratio test is popularly used for model checking for regressions. However, there are two issues that may be the barriers for its powerfulness. F…
Model checking for generalized linear models: a dimension-reduction model-adaptive approach
Xu Guo, Lixing Zhu
Local smoothing testing that is based on multivariate nonparametric regression estimation is one of the main model checking methodologies in the literature. However, relevant tests…
Transformed sufficient dimension reduction
Tao Wang, Xu Guo, Peirong Xu +1
A novel general framework is proposed in this paper for dimension reduction in regression to fill the gap between linear and fully nonlinear dimension reduction. The main idea is t…