5 citations · 11 across the 10 of their papers we have counts for
4 papers · 2 filters
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…
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…