11 citations · 21 across the 18 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…
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…