72 citations · 132 across the 10 of their papers we have counts for
10 papers
On tail-robust autocovariance matrix estimation for high-dimensional and potentially nonstationary time series
Haotian Xu, Stephane Guerrier, Runze Li +1
In this paper, we study the autocovariance matrix estimation and inference problems under heavy-tailedness, high-dimensionality, general nonlinear temporal dependence, and potentia…
A Generalized Knockoff Procedure for FDR Control in Structural Change Detection
Jingyuan Liu, Ao Sun, Yuan Ke
Controlling false discovery rate (FDR) is crucial for variable selection, multiple testing, among other signal detection problems. In literature, there is certainly no shortage of…
Improving COVID-19 Forecasting using eXogenous Variables
Mohammadhossein Toutiaee, Xiaochuan Li, Yogesh Chaudhari +7
In this work, we study the pandemic course in the United States by considering national and state levels data. We propose and compare multiple time-series prediction techniques whi…
Best Subset Selection: Statistical Computing Meets Quantum Computing
Wenxuan Zhong, Yuan Ke, Ye Wang +3
With the rapid development of quantum computers, quantum algorithms have been studied extensively. However, quantum algorithms tackling statistical problems are still lacking. In t…
Large-scale optimal transport map estimation using projection pursuit
Cheng Meng, Yuan Ke, Jingyi Zhang +3
This paper studies the estimation of large-scale optimal transport maps (OTM), which is a well-known challenging problem owing to the curse of dimensionality. Existing literature a…
Model-free Feature Screening and FDR Control with Knockoff Features
Wanjun Liu, Yuan Ke, Jingyuan Liu +1
This paper proposes a model-free and data-adaptive feature screening method for ultra-high dimensional datasets. The proposed method is based on the projection correlation which me…
User-Friendly Covariance Estimation for Heavy-Tailed Distributions
Yuan Ke, Stanislav Minsker, Zhao Ren +2
We offer a survey of recent results on covariance estimation for heavy-tailed distributions. By unifying ideas scattered in the literature, we propose user-friendly methods that fa…
FarmTest: Factor-Adjusted Robust Multiple Testing with Approximate False Discovery Control
Jianqing Fan, Yuan Ke, Qiang Sun +1
Large-scale multiple testing with correlated and heavy-tailed data arises in a wide range of research areas from genomics, medical imaging to finance. Conventional methods for esti…
Factor-Adjusted Regularized Model Selection
Jianqing Fan, Yuan Ke, Kaizheng Wang
This paper studies model selection consistency for high dimensional sparse regression when data exhibits both cross-sectional and serial dependency. Most commonly-used model select…
Model selection and structure specification in ultra-high dimensional generalised semi-varying coefficient models
Degui Li, Yuan Ke, Wenyang Zhang
In this paper, we study the model selection and structure specification for the generalised semi-varying coefficient models (GSVCMs), where the number of potential covariates is al…