1.5k citations · 7.9k across the 139 of their papers we have counts for
72 papers · 1 filter
Inferences on Mixing Probabilities and Ranking in Mixed-Membership Models
Sohom Bhattacharya, Jianqing Fan, Jikai Hou
Network data is prevalent in numerous big data applications including economics and health networks where it is of prime importance to understand the latent structure of network. I…
Isotonic Mechanism for Exponential Family Estimation in Machine Learning Peer Review
Yuling Yan, Weijie J. Su, Jianqing Fan
In 2023, the International Conference on Machine Learning (ICML) required authors with multiple submissions to rank their submissions based on perceived quality. In this paper, we…
Environment Invariant Linear Least Squares
Jianqing Fan, Cong Fang, Yihong Gu +1
This paper considers a multi-environment linear regression model in which data from multiple experimental settings are collected. The joint distribution of the response variable an…
Deep Neural Networks for Nonparametric Interaction Models with Diverging Dimension
Sohom Bhattacharya, Jianqing Fan, Debarghya Mukherjee
Deep neural networks have achieved tremendous success due to their representation power and adaptation to low-dimensional structures. Their potential for estimating structured regr…
Robust High-dimensional Tuning Free Multiple Testing
Jianqing Fan, Zhipeng Lou, Mengxin Yu
A stylized feature of high-dimensional data is that many variables have heavy tails, and robust statistical inference is critical for valid large-scale statistical inference. Yet,…
Factor Augmented Sparse Throughput Deep ReLU Neural Networks for High Dimensional Regression
Jianqing Fan, Yihong Gu
This paper introduces a Factor Augmented Sparse Throughput (FAST) model that utilizes both latent factors and sparse idiosyncratic components for nonparametric regression. The FAST…
Robust Matrix Completion with Heavy-tailed Noise
Bingyan Wang, Jianqing Fan
This paper studies low-rank matrix completion in the presence of heavy-tailed and possibly asymmetric noise, where we aim to estimate an underlying low-rank matrix given a set of h…
How do noise tails impact on deep ReLU networks?
Jianqing Fan, Yihong Gu, Wen-Xin Zhou
This paper investigates the stability of deep ReLU neural networks for nonparametric regression under the assumption that the noise has only a finite p-th moment. We unveil how the…
A provable two-stage algorithm for penalized hazards regression
Jianqing Fan, Wenyan Gong, Qiang Sun
From an optimizer's perspective, achieving the global optimum for a general nonconvex problem is often provably NP-hard using the classical worst-case analysis. In the case of Cox'…
Inference for Heteroskedastic PCA with Missing Data
Yuling Yan, Yuxin Chen, Jianqing Fan
This paper studies how to construct confidence regions for principal component analysis (PCA) in high dimension, a problem that has been vastly under-explored. While computing meas…
Adaptive Robust Large Volatility Matrix Estimation Based on High-Frequency Financial Data
Minseok Shin, Donggyu Kim, Jianqing Fan
Several novel statistical methods have been developed to estimate large integrated volatility matrices based on high-frequency financial data. To investigate their asymptotic behav…
Canonical thresholding for non-sparse high-dimensional linear regression
Igor Silin, Jianqing Fan
We consider a high-dimensional linear regression problem. Unlike many papers on the topic, we do not require sparsity of the regression coefficients; instead, our main structural a…
An theory of PCA and spectral clustering
Emmanuel Abbe, Jianqing Fan, Kaizheng Wang
Principal Component Analysis (PCA) is a powerful tool in statistics and machine learning. While existing study of PCA focuses on the recovery of principal components and their asso…
Bootstrapping -Statistics in High Dimensions
Alexander Giessing, Jianqing Fan
This paper considers a new bootstrap procedure to estimate the distribution of high-dimensional -statistics, i.e. the -norms of the sum of independent -dimen…
Hypothesis testing for eigenspaces of covariance matrix
Igor Silin, Jianqing Fan
Eigenspaces of covariance matrices play an important role in statistical machine learning, arising in variety of modern algorithms. Quantitatively, it is convenient to describe the…
Statistical Inference for High-Dimensional Matrix-Variate Factor Model
Elynn Y. Chen, Jianqing Fan
This paper considers the estimation and inference of the low-rank components in high-dimensional matrix-variate factor models, where each dimension of the matrix-variates ($p \time…
Optimal estimation of functionals of high-dimensional mean and covariance matrix
Jianqing Fan, Haolei Weng, Yifeng Zhou
Motivated by portfolio allocation and linear discriminant analysis, we consider estimating a functional involving both the mean vector $\mathbfμ…
Learning Latent Factors from Diversified Projections and its Applications to Over-Estimated and Weak Factors
Jianqing Fan, Yuan Liao
Estimations and applications of factor models often rely on the crucial condition that the number of latent factors is consistently estimated, which in turn also requires that fact…
Adaptive Huber Regression on Markov-dependent Data
Jianqing Fan, Yongyi Guo, Bai Jiang
High-dimensional linear regression has been intensively studied in the community of statistics in the last two decades. For the convenience of theoretical analyses, classical metho…
Asymptotic Theory of Eigenvectors for Random Matrices with Diverging Spikes
Jianqing Fan, Yingying Fan, Xiao Han +1
Characterizing the asymptotic distributions of eigenvectors for large random matrices poses important challenges yet can provide useful insights into a range of statistical applica…
Asymmetry Helps: Eigenvalue and Eigenvector Analyses of Asymmetrically Perturbed Low-Rank Matrices
Yuxin Chen, Chen Cheng, Jianqing Fan
This paper is concerned with the interplay between statistical asymmetry and spectral methods. Suppose we are interested in estimating a rank-1 and symmetric matrix $\mathbf{M}^{\s…
Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval
Jianqing Fan, Han Liu, Zhaoran Wang +1
We study the fundamental tradeoffs between statistical accuracy and computational tractability in the analysis of high dimensional heterogeneous data. As examples, we study sparse…
Tensor Methods for Additive Index Models under Discordance and Heterogeneity
Krishnakumar Balasubramanian, Jianqing Fan, Zhuoran Yang
Motivated by the sampling problems and heterogeneity issues common in high- dimensional big datasets, we consider a class of discordant additive index models. We propose method of…
Bernstein's inequalities for general Markov chains
Bai Jiang, Qiang Sun, Jianqing Fan
We establish Bernstein's inequalities for functions of general (general-state-space and possibly non-reversible) Markov chains. These inequalities achieve sharp variance proxies an…