16 citations · 23 across the 11 of their papers we have counts for
7 papers · 1 filter
A General Stability Approach to False Discovery Rate Control
Jiajun Sun, Zhanrui Cai, Wei Zhong
Stability and reproducibility are essential considerations in various applications of statistical methods. False Discovery Rate (FDR) control methods are able to control false sign…
Knockoffs Inference under Privacy Constraints
Zhanrui Cai, Yingying Fan, Lan Gao
Model-X knockoff framework offers a model-free variable selection method that ensures finite sample false discovery rate (FDR) control. However, the complexity of generating knocko…
Penalized Sparse Covariance Regression with High Dimensional Covariates
Yuan Gao, Zhiyuan Zhang, Zhanrui Cai +3
Covariance regression offers an effective way to model the large covariance matrix with the auxiliary similarity matrices. In this work, we propose a sparse covariance regression (…
Model-free Change-point Detection using AUC of a Classifier
Rohit Kanrar, Feiyu Jiang, Zhanrui Cai
In contemporary data analysis, it is increasingly common to work with non-stationary complex data sets. These data sets typically extend beyond the classical low-dimensional Euclid…
Robust Model Selection with Application in Single-Cell Multiomics Data
Zhanrui Cai
Model selection is critical in the modern statistics and machine learning community. However, most existing works do not apply to heavy-tailed data, which are commonly encountered…
Asymptotic Distribution-Free Independence Test for High Dimension Data
Zhanrui Cai, Jing Lei, Kathryn Roeder
Test of independence is of fundamental importance in modern data analysis, with broad applications in variable selection, graphical models, and causal inference. When the data is h…
A Distribution Free Conditional Independence Test with Applications to Causal Discovery
Zhanrui Cai, Runze Li, Yaowu Zhang
This paper is concerned with test of the conditional independence. We first establish an equivalence between the conditional independence and the mutual independence. Based on the…