Publicatons

Papers under Review    (* : Corresponding author;    ** : Graduate student)

• Hyunju Kang and Kyoungjae Lee*.  Bayesian multivariate spatio-temporal model for quasi-sparse count data. (in Korean) Submitted.

• Kwangmin Lee, Kyeongwon Lee, Kyoungjae Lee and Seongil Jo. bspcov: An R Package for Bayesian sparse covariance matrix estimation. Submitted. [R package]

• Xuan Cao and Kyoungjae Lee*. Scalable Bayesian inference on high-dimensional multivariate linear regression. Submitted.

• Seyong Hwang, Kyoungjae Lee, Sunmin Oh and Gunwoong Park. Bayesian approach to linear Bayesian networks. Submitted.

• Kyoungjae Lee, Seongil Jo, Kyeongwon Lee and Jaeyong Lee. Scalable and optimal Bayesian inference for sparse covariance matrices via screened beta-mixture prior. Submitted. [R package]

Publications

• Kyoungjae Lee, Won Chang and Xuan Cao. (2024+) The joint local dependence Cholesky prior for bandwidth selection across multiple groups. Bayesian Analysis. Accepted.

• Xuan Cao and Kyoungjae Lee*. (2024+) Consistent and scalable Bayesian joint variable and graph selection for disease diagnosis leveraging functional brain network. Bayesian Analysis. Accepted. [R code]

• Kyoungjae Lee, Kisung You and Lizhen Lin. (2024+) Bayesian optimal two-sample tests for high-dimensional Gaussian populations. Bayesian Analysis. Accepted. [R package]

• Miru Ma**, Mingi Kang** and Kyoungjae Lee*. (2024) Comparing MCMC algorithms for the horseshoe prior. (in Korean) The Korean Journal of Applied Statistics. 37(1), 103-118.

• Xuan Cao and Kyoungjae Lee*. (2024) Bayesian inference on hierarchical nonlocal priors in generalized linear models. Bayesian Analysis. 19(1), 99-122. [R code]

• Xuan Cao, Liangliang Zhang and Kyoungjae Lee*. (2024) Development of network-guided transcriptomic risk score for disease prediction. Stat. 13(1), 1-20.

• Kwangmin Lee, Kyoungjae Lee and Jaeyong Lee. (2023) Post-processed posteriors for banded covariances. Bayesian Analysis. 18(3), 1017-1040. [R code]

• Jaeoh Kim, Seongil Jo and Kyoungjae Lee*. (2023) Bayesian computational methods for state-space models with application to SIR model. Journal of Statistical Computation and Simulation. 93(7), 1207-1223.

• Jongmin Lee, Joonpyo Kim, Joonho Shin, Seongjin Cho**, Seongmin Kim and Kyoungjae Lee. (2023) Analysis of wildfires and their extremes via spatial quantile autoregressive model. Extremes. 26(2), 353-379.

• Kyoungjae Lee and Lizhen Lin. (2023) Scalable Bayesian high-dimensional local dependence learning. Bayesian Analysis.  18(1), 25-47. [R code]

• Kyoungjae Lee, Seongil Jo and Jaeyong Lee. (2022) The beta-mixture shrinkage prior for sparse covariances with near-minimax posterior convergence rate. Journal of Multivariate Analysis. 192, 105067. [R package]

• Insong Jang** and Kyoungjae Lee*. (2022) Introduction to variational Bayes for high-dimensional linear and logistic regression models. (in Korean) The Korean Journal of Applied Statistics. 35(3), 445-455.

• Kyoungjae Lee and Xuan Cao. (2022) Bayesian joint inference for multiple directed acyclic graphs. Journal of Multivariate Analysis. 191, 105003. [R code]

• Bongsu Kim** and Kyoungjae Lee*. (2022) A comparison study of Bayesian variable selection methods for sparse covariance matrices. (in Korean) The Korean Journal of Applied Statistics. 35(2), 285-298.

• Kwangmin Lee, Kyoungjae Lee and Jaeyong Lee. (2022) Estimation of conditional mean operator under the bandable covariance structure. Electronic Journal of Statistics. 16(1), 1253-1302.

• Kyoungjae Lee, Lizhen Lin and David Dunson. (2021) Maximum pairwise Bayes factors for covariance structure testing. Electronic Journal of Statistics. 15(2), 4384-4419. [R code]

• Ju-Won Shin** and Kyoungjae Lee*. (2021) A comparison study of Bayesian high-dimensional linear regression models. (in Korean) The Korean Journal of Applied Statistics. 34(3), 491-505.

• Xuan Cao and Kyoungjae Lee*. (2021) Joint Bayesian variable and DAG selection consistency for high-dimensional regression models with network-structured covariates. Statistica Sinica. 31(3),  1509-1530. [R code]

• Kyoungjae Lee and Xuan Cao. (2021) Bayesian group selection in logistic regression with application to MRI data analysis. Biometrics. 77(2), 391–400. [R code]

• Kyoungjae Lee, Minwoo Chae and Lizhen Lin. (2021) Bayesian high-dimensional semi-parametric Inference beyond sub-Gaussian errors. Journal of the Korean Statistical Society. 50(2), 511-527.

• Xuan Cao, Kyoungjae Lee* and Qingling Huang. (2021) Bayesian variable selection in logistic regression with application to whole-brain functional connectivity analysis for Parkinson’s Disease. Statistical Methods in Medical Research. 30(3), 826-842.

• Kyoungjae Lee and Xuan Cao. (2021) Bayesian inference for high-dimensional decomposable graphs. Electronic Journal of Statistics. 15(1), 1549–1582.

• Kyoungjae Lee and Jaeyong Lee. (2021) Estimating large precision matrices via modified Cholesky decomposition. Statistica Sinica. 31(1), 173–196. [R package]

• Kyoungjae Lee and Lizhen Lin. (2020) Bayesian bandwidth test and selection for high-dimensional banded precision matrices. Bayesian Analysis. 15(3), 737–758.

• Xuan Cao and Kyoungjae Lee*. (2020) Variable selection using nonlocal priors in high-dimensional generalized linear models with application to fMRI data analysis. Entropy. 22(8), 807; https://doi.org/10.3390/e22080807. [R code]

• Kyoungjae Lee, Jaeyong Lee and Lizhen Lin. (2019) Minimax posterior convergence rates and model selection consistency in high-dimensional DAG models based on sparse Cholesky factors. The Annals of Statistics. 47(6), 3413-3437.

• Kyoungjae Lee, Jaeyong Lee and Sarat C. Dass. (2018). Inference for differential equation models using relaxation via dynamical systems. Computational Statistics & Data Analysis. 127, 116-134.

• Kyoungjae Lee and Jaeyong Lee. (2018). Optimal Bayesian minimax rates for unconstrained large covariance matrices. Bayesian Analysis. 13(4), 1211-1229.

• Sarat C. Dass, Jaeyong Lee, Kyoungjae Lee* and Jonghun Park. (2017). Laplace based approximate posterior inference for differential equation models. Statistics and Computing. 27(3), 679-698. [R package]

• Sarat C. Dass, Jaeyong Lee and Kyoungjae Lee. (2016). Bayesian inference using two-stage Laplace approximation for differential equation models. AIP Conference Proceedings. Eds. Aamir Hussain Bhat, et al. Vol. 1787. No. 1. AIP Publishing.

• Youngseon Lee, Kyoungjae Lee, Kwangmin Lee, Jaeyong Lee and Jinwook Seo. (2015). Introduction to the Indian buffet process: theory and applications. (in Korean) The Korean Journal of Applied Statistics. 28(2), 251-268.

• Jaeyong Lee, Kyoungjae Lee and Youngseon Lee. (2014). History and future of Bayesian Statistics. (in Korean) The Korean Journal of Applied Statistics. 27(6), 855-863.

• Namyee Kim, Geummun Nam, Yuna Kim, Dongkye Lee, Sehyoun Park, Kyoungjae Lee and Jaeyong Lee. (2014). Identification and classification of fresh lubricants and used engine oils by GC/MS and Bayesian model. (in Korean) Analytical Science and Technology. 27(1), 41-59.