11 citations · 21 across the 18 of their papers we have counts for
21 papers
Bayesian optimal change point detection in high-dimensions
Jaehoon Kim, Kyoungjae Lee, Lizhen Lin
We propose the first Bayesian methods for detecting change points in high-dimensional mean and covariance structures. These methods are constructed using pairwise Bayes factors, le…
Scalable and optimal Bayesian inference for sparse covariance matrices via screened beta-mixture prior
Kyoungjae Lee, Seongil Jo, Kyeongwon Lee +1
In this paper, we propose a scalable Bayesian method for sparse covariance matrix estimation by incorporating a continuous shrinkage prior with a screening procedure. In the first…
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 Optimal Two-sample Tests in High-dimension
Kyoungjae Lee, Kisung You, Lizhen Lin
We propose optimal Bayesian two-sample tests for testing equality of high-dimensional mean vectors and covariance matrices between two populations. In many applications including g…
Scalable Bayesian high-dimensional local dependence learning
Kyoungjae Lee, Lizhen Lin
In this work, we propose a scalable Bayesian procedure for learning the local dependence structure in a high-dimensional model where the variables possess a natural ordering. The o…
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…
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…
Minimax Posterior Convergence Rates and Model Selection Consistency in High-dimensional DAG Models based on Sparse Cholesky Factors
Kyoungjae Lee, Jaeyong Lee, Lizhen Lin
In this paper, we study the high-dimensional sparse directed acyclic graph (DAG) models under the empirical sparse Cholesky prior. Among our results, strong model selection consist…
Maximum Pairwise Bayes Factors for Covariance Structure Testing
Kyoungjae Lee, Lizhen Lin, David Dunson
Hypothesis testing of structure in covariance matrices is of significant importance, but faces great challenges in high-dimensional settings. Although consistent frequentist one-sa…
Bayesian Bandwidth Test and Selection for High-dimensional Banded Precision Matrices
Kyoungjae Lee, Lizhen Lin
Assuming a banded structure is one of the common practice in the estimation of high-dimensional precision matrix. In this case, estimating the bandwidth of the precision matrix is…
Estimating Large Precision Matrices via Modified Cholesky Decomposition
Kyoungjae Lee, Jaeyong Lee
We introduce the -banded Cholesky prior for estimating a high-dimensional bandable precision matrix via the modified Cholesky decomposition. The bandable assumption is imposed o…
Inference for Differential Equation Models using Relaxation via Dynamical Systems
Kyoungjae Lee, Jaeyong Lee, Sarat C. Dass
Statistical regression models whose mean functions are represented by ordinary differential equations (ODEs) can be used to describe phenomenons dynamical in nature, which are abun…
Optimal Bayesian Minimax Rates for Unconstrained Large Covariance Matrices
Kyoungjae Lee, Jaeyong Lee
We obtain the optimal Bayesian minimax rate for the unconstrained large covariance matrix of multivariate normal sample with mean zero, when both the sample size, n, and the dimens…
Laplace based approximate posterior inference for differential equation models
Sarat C. Dass, Jaeyong Lee, Kyoungjae Lee +1
Ordinary differential equations are arguably the most popular and useful mathematical tool for describing physical and biological processes in the real world. Often, these physical…