14 citations · 46 across the 16 of their papers we have counts for
16 papers
An adaptive subsampling method for large-sample feature screening
Xiaxue Ouyang, Kejun He, Cheng Meng
We consider the sure independence screening (SIS) method, a standard feature screening approach that aims to eliminate non-informative features in ultrahigh-dimensional datasets. A…
Penalized spline estimation of principal components for sparse functional data: rates of convergence
Shiyuan He, Jianhua Z. Huang, Kejun He
This paper gives a comprehensive treatment of the convergence rates of penalized spline estimators for simultaneously estimating several leading principal component functions, when…
Joint Mirror Procedure: Controlling False Discovery Rate for Identifying Simultaneous Signals
Linsui Deng, Kejun He, Xianyang Zhang
In many applications, the process of identifying a specific feature of interest often involves testing multiple hypotheses for their joint statistical significance. Examples includ…
Principal Component Analysis of Two-dimensional Functional Data with Serial Correlation
Shirun Shen, Huiya Zhou, Kejun He +1
In this paper, we propose a novel model to analyze serially correlated two-dimensional functional data observed sparsely and irregularly on a domain which may not be a rectangle. O…
Bayesian Nonlinear Tensor Regression with Functional Fused Elastic Net Prior
Shuoli Chen, Kejun He, Shiyuan He +2
Tensor regression methods have been widely used to predict a scalar response from covariates in the form of a multiway array. In many applications, the regions of tensor covariates…
Spline Estimation of Functional Principal Components via Manifold Conjugate Gradient Algorithm
Shiyuan He, Hanxuan Ye, Kejun He
Functional principal component analysis has become the most important dimension reduction technique in functional data analysis. Based on B-spline approximation, functional princip…
A Unified Analysis of Multi-task Functional Linear Regression Models with Manifold Constraint and Composite Quadratic Penalty
Shiyuan He, Hanxuan Ye, Kejun He
This work studies the multi-task functional linear regression models where both the covariates and the unknown regression coefficients (called slope functions) are curves. For slop…
Functional Bayesian Networks for Discovering Causality from Multivariate Functional Data
Fangting Zhou, Kejun He, Kunbo Wang +2
Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest, which…
Powerful Spatial Multiple Testing via Borrowing Neighboring Information
Linsui Deng, Kejun He, Xianyang Zhang
Clustered effects are often encountered in multiple hypothesis testing of spatial signals. In this paper, we propose a new method, termed \textit{two-dimensional spatial multiple t…
Causal Discovery with Heterogeneous Observational Data
Fangting Zhou, Kejun He, Yang Ni
We consider the problem of causal discovery (structure learning) from heterogeneous observational data. Most existing methods assume a homogeneous sampling scheme, which leads to m…
LinDA: linear models for differential abundance analysis of microbiome compositional data
Huijuan Zhou, Kejun He, Jun Chen +1
Differential abundance analysis is at the core of statistical analysis of microbiome data. The compositional nature of microbiome sequencing data makes false positive control chall…
CP Degeneracy in Tensor Regression
Ya Zhou, Raymond K. W. Wong, Kejun He
Tensor linear regression is an important and useful tool for analyzing tensor data. To deal with high dimensionality, CANDECOMP/PARAFAC (CP) low-rank constraints are often imposed…
Broadcasted Nonparametric Tensor Regression
Ya Zhou, Raymond K. W. Wong, Kejun He
We propose a novel use of a broadcasting operation, which distributes univariate functions to all entries of the tensor covariate, to model the nonlinearity in tensor regression no…
Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization
Fangting Zhou, Kejun He, Qiwei Li +2
High-throughput sequencing technology provides unprecedented opportunities to quantitatively explore human gut microbiome and its relation to diseases. Microbiome data are composit…
Efficient Estimation of Mixture Cure Frailty Model for Clustered Current Status Data
Tong Wang, Kejun He, Wei Ma +2
Current status data abounds in the field of epidemiology and public health, where the only observable data for a subject is the random inspection time and the event status at inspe…
Asymptotic properties of adaptive group Lasso for sparse reduced rank regression
Kejun He, Jianhua Z. Huang
This paper studies the asymptotic properties of the penalized least squares estimator using an adaptive group Lasso penalty for the reduced rank regression. The group Lasso penalty…