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20192022
most citedCausal Discovery with Heterogeneous Observational Data

5 citations · 5 across the 3 of their papers we have counts for

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5 papers · 1 filter

stat.ME2022

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…

stat.ME2022

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…

stat.ME20225 cited

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…

stat.ME2020

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…

stat.ME2019

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…