2 citations · 5 across the 13 of their papers we have counts for
13 papers
Limit theorems of Azadkia-Chatterjee's conditional graph correlation
Muhong Gao, Fang Han, Qizhai Li
Inferring the strength of conditional dependence and testing conditional independence are fundamental problems in statistics. A recent breakthrough by Azadkia and Chatterjee introd…
A Parameter-Centric View on Regression
Jingxin Yan, Lin Liu, Oliver Dukes +2
Discussion on ``Regression by Composition'' by Farewell, Daniel, Stensrud, and Huitfeldt
Principled Inference in Dense High-Dimensional Linear Models via Local Conditional Sparsity
Wenjun Xiong, Yan Chen, Mingya Long +1
High-dimensional inference methods often rely on coefficient sparsity, an assumption that can be restrictive when signals are dense but individually weak. In such settings, valid i…
Inverse regression for causal inference with multiple outcomes
Wei Zhang, Qizhai Li, Peng Ding
With multiple outcomes in empirical research, a common strategy is to define a composite outcome as a weighted average of the original outcomes. However, the choices of weights are…
Gaussian Multiplier Bootstrap Procedure for the th Largest Coordinate of High-Dimensional Statistics
Yixi Ding, Qizhai Li, Yuke Shi +2
We consider the problem of Gaussian multiplier bootstrap procedures for the th largest statistics and functions of the top order statistics, which are commonly encountered i…
Tight Generalization Error Bounds for Stochastic Gradient Descent in Non-convex Learning
Wenjun Xiong, Juan Ding, Xinlei Zuo +1
Stochastic Gradient Descent (SGD) is fundamental for training deep neural networks, especially in non-convex settings. Understanding SGD's generalization properties is crucial for…
Gaussian Approximations for the th coordinate of sums of random vectors
Yixi Ding, Qizhai Li, Yuke Shi +1
We consider the problem of Gaussian approximation for the th coordinate of a sum of high-dimensional random vectors. Such a problem has been studied previously for (i.e.,…
A Bayesian Approach Toward Robust Multidimensional Ellipsoid-Specific Fitting
Zhao Mingyang, Jia Xiaohong, Ma Lei +5
This work presents a novel and effective method for fitting multidimensional ellipsoids to scattered data in the contamination of noise and outliers. We approach the problem as a B…
Functional knockoffs selection with applications to functional data analysis in high dimensions
Xinghao Qiao, Mingya Long, Qizhai Li
The knockoffs is a recently proposed powerful framework that effectively controls the false discovery rate (FDR) for variable selection. However, none of the existing knockoff solu…
A family of Chatterjee's correlation coefficients and their properties
Muhong Gao, Qizhai Li
Quantifying the strength of functional dependence between random scalars and is an important statistical problem. While many existing correlation coefficients excel in iden…
The Cauchy Combination Test under Arbitrary Dependence Structures
Mingya Long, Zhengbang Li, Wei Zhang +1
Aggregating multiple effects is often encountered in large-scale data analysis where the fraction of significant effects is generally small. Many existing methods cannot handle it…
Distance-based regression analysis for measuring associations
Yuke Shi, Wei Zhang, Aiyi Liu +1
Distance-based regression model, as a nonparametric multivariate method, has been widely used to detect the association between variations in a distance or dissimilarity matrix for…
An Imputation-Consistency Algorithm for High-Dimensional Missing Data Problems and Beyond
Faming Liang, Bochao Jia, Jingnan Xue +2
Missing data are frequently encountered in high-dimensional problems, but they are usually difficult to deal with using standard algorithms, such as the expectation-maximization (E…