Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Lee, Kyoungjae"'
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, leveraging modularization to identify significant changes in individual
Externí odkaz:
http://arxiv.org/abs/2411.14864
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 step of the procedure, the off-diagonal elements with small correlatio
Externí odkaz:
http://arxiv.org/abs/2206.12773
Autor:
Cao, Xuan, Lee, Kyoungjae
Variable selection methods with nonlocal priors have been widely studied in linear regression models, and their theoretical and empirical performances have been reported. However, the crucial model selection properties for hierarchical nonlocal prior
Externí odkaz:
http://arxiv.org/abs/2203.07110
Autor:
Cao, Xuan, Lee, Kyoungjae
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 relevant to the binary response and functionally related to one another.
Externí odkaz:
http://arxiv.org/abs/2203.07108
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 genomics and medical imaging, it is natural to assume that only a few e
Externí odkaz:
http://arxiv.org/abs/2112.02580
Autor:
Lee, Kyoungjae, Lin, Lizhen
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 ordering of variables can be indexed by time, the vicinities of spatial
Externí odkaz:
http://arxiv.org/abs/2109.11795
We consider high-dimensional multivariate linear regression models, where the joint distribution of covariates and response variables is a multivariate normal distribution with a bandable covariance matrix. The main goal of this paper is to estimate
Externí odkaz:
http://arxiv.org/abs/2103.06420
Statistical inference for sparse covariance matrices is crucial to reveal dependence structure of large multivariate data sets, but lacks scalable and theoretically supported Bayesian methods. In this paper, we propose beta-mixture shrinkage prior, c
Externí odkaz:
http://arxiv.org/abs/2101.04351
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, posterior samples are obtained from the conjugate inverse-Wishart post
Externí odkaz:
http://arxiv.org/abs/2011.12627
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 $\beta$-H\"{o}lder class with an exponentially decreasing tail, which does n
Externí odkaz:
http://arxiv.org/abs/2008.13174