activity
20192022
most citedJoint Bayesian Variable and DAG Selection Consistency for High-dimensional Regression Models with Network-structured Covariates

4 citations · 5 across the 7 of their papers we have counts for

collaborators

8 papers

stat.ME2022

Bayesian inference on hierarchical nonlocal priors in generalized linear models

Xuan Cao, Kyoungjae Lee

Variable selection methods with nonlocal priors have been widely studied in linear regression models, and their theoretical and empirical performances have been reported. However,…

stat.ME2022

Consistent and scalable Bayesian joint variable and graph selection for disease diagnosis leveraging functional brain network

Xuan Cao, Kyoungjae Lee

We consider the joint inference of regression coefficients and the inverse covariance matrix for covariates in high-dimensional probit regression, where the predictors are both rel…

stat.ME2020

Bayesian joint inference for multiple directed acyclic graphs

Kyoungjae Lee, Xuan Cao

In many applications, data often arise from multiple groups that may share similar characteristics. A joint estimation method that models several groups simultaneously can be more…

math.ST20204 cited

Joint Bayesian Variable and DAG Selection Consistency for High-dimensional Regression Models with Network-structured Covariates

Xuan Cao, Kyoungjae Lee

We consider the joint sparse estimation of regression coefficients and the covariance matrix for covariates in a high-dimensional regression model, where the predictors are both re…

math.ST2020

Bayesian inference for high-dimensional decomposable graphs

Kyoungjae Lee, Xuan Cao

In this paper, we consider high-dimensional Gaussian graphical models where the true underlying graph is decomposable. A hierarchical -Wishart prior is proposed to conduct a Bay…

stat.ME2019

Bayesian Group Selection in Logistic Regression with Application to MRI Data Analysis

Kyoungjae Lee, Xuan Cao

We consider Bayesian logistic regression models with group-structured covariates. In high-dimensional settings, it is often assumed that only small portion of groups are significan…