6 citations · 13 across the 10 of their papers we have counts for
10 papers
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,…
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…
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…
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…
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…
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…
Consistent Bayesian Sparsity Selection for High-dimensional Gaussian DAG Models with Multiplicative and Beta-mixture Priors
Xuan Cao, Kshitij Khare, Malay Ghosh
Estimation of the covariance matrix for high-dimensional multivariate datasets is a challenging and important problem in modern statistics. In this paper, we focus on high-dimensio…
A permutation-based Bayesian approach for inverse covariance estimation
Xuan Cao, Shaojun Zhang
Covariance estimation and selection for multivariate datasets in a high-dimensional regime is a fundamental problem in modern statistics. Gaussian graphical models are a popular cl…
High-dimensional posterior consistency for hierarchical non-local priors in regression
Xuan Cao, Kshitij Khare, Malay Ghosh
The choice of tuning parameters in Bayesian variable selection is a critical problem in modern statistics. In particular, for Bayesian linear regression with non-local priors, the…
Posterior Graph Selection and Estimation Consistency for High-dimensional Bayesian DAG Models
Xuan Cao, Kshitij Khare, Malay Ghosh
Covariance estimation and selection for high-dimensional multivariate datasets is a fundamental problem in modern statistics. Gaussian directed acyclic graph (DAG) models are a pop…