1 citations · 1 across the 7 of their papers we have counts for
7 papers
Optimal Estimation of Parameters in Degree Corrected Mixed Membership Models
Stephen Jiang, Jianqing Fan
With the rise of big data, networks have pervaded many aspects of our daily lives, with applications ranging from the social to natural sciences. Understanding the latent structure…
Conditional nonparametric variable screening by neural factor regression
Jianqing Fan, Weining Wang, Yue Zhao
High-dimensional covariates often admit linear factor structure. To effectively screen correlated covariates in high-dimension, we propose a conditional variable screening test bas…
Tests for principal eigenvalues and eigenvectors
Jianqing Fan, Yingying Li, Ningning Xia +1
We establish central limit theorems for principal eigenvalues and eigenvectors under a large factor model setting, and develop two-sample tests of both principal eigenvalues and pr…
Two-way Homogeneity Pursuit for Quantile Network Vector Autoregression
Wenyang Liu, Ganggang Xu, Jianqing Fan +1
We propose a two-way grouped network quantile (TGNQ) autoregression model for time series data observed on networks with substantial heterogeneity and complex directional interacti…
Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks
Jianqing Fan, Zhaoran Wang, Zhuoran Yang +1
We study high-dimensional multi-armed contextual bandits with batched feedback where the steps of online interactions are divided into batches. In specific, each batch coll…
Neyman-Pearson and equal opportunity: when efficiency meets fairness in classification
Jianqing Fan, Xin Tong, Yanhui Wu +2
Organizations often rely on statistical algorithms to make socially and economically impactful decisions. We must address the fairness issues in these important automated decisions…
A Bootstrap Hypothesis Test for High-Dimensional Mean Vectors
Alexander Giessing, Jianqing Fan
This paper is concerned with testing global null hypotheses about population mean vectors of high-dimensional data. Current tests require either strong mixing (independence) condit…