4 citations · 8 across the 12 of their papers we have counts for
7 papers · 1 filter
Estimation of Conditional Mean Operator under the Bandable Covariance Structure
Kwangmin Lee, Kyoungjae Lee, Jaeyong Lee
We consider high-dimensional multivariate linear regression models, where the joint distribution of covariates and response variables is a multivariate normal distribution with a b…
The Beta-Mixture Shrinkage Prior for Sparse Covariances with Posterior Minimax Rates
Kyoungjae Lee, Seongil Jo, Jaeyong Lee
Statistical inference for sparse covariance matrices is crucial to reveal dependence structure of large multivariate data sets, but lacks scalable and theoretically supported Bayes…
Post-Processed Posteriors for Banded Covariances
Kwangmin Lee, Kyoungjae Lee, Jaeyong Lee
We consider Bayesian inference of banded covariance matrices and propose a post-processed posterior. The post-processing of the posterior consists of two steps. In the first step,…
Bayesian High-dimensional Semi-parametric Inference beyond sub-Gaussian Errors
Kyoungjae Lee, Minwoo Chae, Lizhen Lin
We consider a sparse linear regression model with unknown symmetric error under the high-dimensional setting. The true error distribution is assumed to belong to the locally -Hö…
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…