2 citations · 2 across the 5 of their papers we have counts for
6 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 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 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…
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…