62 citations · 153 across the 19 of their papers we have counts for
8 papers · 1 filter
Post-Processed Posteriors for Sparse Covariances and Its Application to Global Minimum Variance Portfolio
Kwangmin Lee, Jaeyong Lee
We consider Bayesian inference of sparse covariance matrices and propose a post-processed posterior. This method consists of two steps. In the first step, posterior samples are obt…
Identifiability of Covariance Kernels in the Gaussian Process Regression Model
Jaehoan Kim, Jaeyong Lee
Gaussian process regression (GPR) model is a popular nonparametric regression model. In GPR, features of the regression function such as varying degrees of smoothness and periodici…
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,…
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…