1.5k citations · 1.7k across the 34 of their papers we have counts for
35 papers
Nonparametric mixture MLEs under Gaussian-smoothed optimal transport distance
Fang Han, Zhen Miao, Yandi Shen
The Gaussian-smoothed optimal transport (GOT) framework, pioneered in Goldfeld et al. (2020) and followed up by a series of subsequent papers, has quickly caught attention among re…
Distribution-free tests of multivariate independence based on center-outward quadrant, Spearman, Kendall, and van der Waerden statistics
Hongjian Shi, Mathias Drton, Marc Hallin +1
Due to the lack of a canonical ordering in for , defining multivariate generalizations of the classical univariate ranks has been a long-standing open problem…
On boosting the power of Chatterjee's rank correlation
Zhexiao Lin, Fang Han
Chatterjee (2021)'s ingenious approach to estimating a measure of dependence first proposed by Dette et al. (2013) based on simple rank statistics has quickly caught attention. Thi…
On Azadkia-Chatterjee's conditional dependence coefficient
Hongjian Shi, Mathias Drton, Fang Han
In recent work, Azadkia and Chatterjee (2021) laid out an ingenious approach to defining consistent measures of conditional dependence. Their fully nonparametric approach forms sta…
Robust Functional Principal Component Analysis via Functional Pairwise Spatial Signs
Guangxing Wang, Sisheng Liu, Fang Han +1
Functional principal component analysis (FPCA) has been widely used to capture major modes of variation and reduce dimensions in functional data analysis. However, standard FPCA ba…
On a phase transition in general order spline regression
Yandi Shen, Qiyang Han, Fang Han
In the Gaussian sequence model in , we study the fundamental limit of approximating the signal by a class of (generalized) s…
Exponential inequalities for dependent V-statistics via random Fourier features
Yandi Shen, Fang Han, Daniela Witten
We establish exponential inequalities for a class of V-statistics under strong mixing conditions. Our theory is developed via a novel kernel expansion based on random Fourier featu…
Distribution-free consistent independence tests via center-outward ranks and signs
Hongjian Shi, Mathias Drton, Fang Han
This paper investigates the problem of testing independence of two random vectors of general dimensions. For this, we give for the first time a distribution-free consistent test. O…
On rank estimators in increasing dimensions
Yanqin Fan, Fang Han, Wei Li +1
The family of rank estimators, including Han's maximum rank correlation (Han, 1987) as a notable example, has been widely exploited in studying regression problems. For these estim…
Probability inequalities for high dimensional time series under a triangular array framework
Fang Han, Weibiao Wu
Study of time series data often involves measuring the strength of temporal dependence, on which statistical properties like consistency and central limit theorem are built. Histor…
Asymptotic joint distribution of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model
Zeng Li, Fang Han, Jianfeng Yao
This paper studies the joint limiting behavior of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model, where the asymptotic reg…
Tail behavior of dependent V-statistics and its applications
Yandi Shen, Fang Han, Daniela Witten
We establish exponential inequalities and Cramer-type moderate deviation theorems for a class of V-statistics under strong mixing conditions. Our theory is developed via kernel exp…
Optimal estimation of variance in nonparametric regression with random design
Yandi Shen, Chao Gao, Daniela Witten +1
Consider the heteroscedastic nonparametric regression model with random design \begin{align*} Y_i = f(X_i) + V^{1/2}(X_i)\varepsilon_i, \quad i=1,2,\ldots,n, \end{align*} with $f(\…
High dimensional consistent independence testing with maxima of rank correlations
Mathias Drton, Fang Han, Hongjian Shi
Testing mutual independence for high-dimensional observations is a fundamental statistical challenge. Popular tests based on linear and simple rank correlations are known to be inc…
Moment bounds for large autocovariance matrices under dependence
Fang Han, Yicheng Li
The goal of this paper is to obtain expectation bounds for the deviation of large sample autocovariance matrices from their means under weak data dependence. While the accuracy of…
On inference validity of weighted U-statistics under data heterogeneity
Fang Han, Tianchen Qian
Motivated by challenges on studying a new correlation measurement being popularized in evaluating online ranking algorithms' performance, this manuscript explores the validity of u…
Pairwise Difference Estimation of High Dimensional Partially Linear Model
Fang Han, Zhao Ren, Yuxin Zhu
This paper proposes a regularized pairwise difference approach for estimating the linear component coefficient in a partially linear model, with consistency and exact rates of conv…
On Estimation of Isotonic Piecewise Constant Signals
Chao Gao, Fang Han, Cun-Hui Zhang
Consider a sequence of real data points with underlying means . This paper starts from studying the setting that is both piecewise cons…
An Exponential Inequality for U-Statistics under Mixing Conditions
Fang Han
The family of U-statistics plays a fundamental role in statistics. This paper proves a novel exponential inequality for U-statistics under the time series setting. Explicit mixing…
On Gaussian Comparison Inequality and Its Application to Spectral Analysis of Large Random Matrices
Fang Han, Sheng Xu, Wen-Xin Zhou
Recently, Chernozhukov, Chetverikov, and Kato [Ann. Statist. 42 (2014) 1564--1597] developed a new Gaussian comparison inequality for approximating the suprema of empirical process…
A Provable Smoothing Approach for High Dimensional Generalized Regression with Applications in Genomics
Fang Han, Hongkai Ji, Zhicheng Ji +1
In many applications, linear models fit the data poorly. This article studies an appealing alternative, the generalized regression model. This model only assumes that there exists…
An Extreme-Value Approach for Testing the Equality of Large U-Statistic Based Correlation Matrices
Cheng Zhou, Fang Han, Xinsheng Zhang +1
There has been an increasing interest in testing the equality of large Pearson's correlation matrices. However, in many applications it is more important to test the equality of la…
Robust Inference of Risks of Large Portfolios
Jianqing Fan, Fang Han, Han Liu +1
We propose a bootstrap-based robust high-confidence level upper bound (Robust H-CLUB) for assessing the risks of large portfolios. The proposed approach exploits rank-based and qua…
Distribution-Free Tests of Independence in High Dimensions
Fang Han, Shizhe Chen, Han Liu
We consider the testing of mutual independence among all entries in a -dimensional random vector based on independent observations. We study two families of distribution-fre…