Kuo-Jung Lee
Kuo-Jung Lee
Department of Statistics, National Cheng Kung University
Verified email at mail.ncku.edu.tw - Homepage
Cited by
Cited by
Spatial Bayesian variable selection models on functional magnetic resonance imaging time-series data
KJ Lee, GL Jones, BS Caffo, SS Bassett
Bayesian Analysis (Online) 9 (3), 699, 2014
Bayesian analysis of Box–Cox transformed linear mixed models with ARMA (p, q) dependence
JC Lee, TI Lin, KJ Lee, YL Hsu
Journal of statistical Planning and Inference 133 (2), 435-451, 2005
Bayesian variable selection for finite mixture model of linear regressions
KJ Lee, RB Chen, YN Wu
Computational Statistics & Data Analysis 95, 1-16, 2016
An instantaneous spatiotemporal model for predicting traffic-related ultrafine particle concentration through mobile noise measurements
MY Lin, YX Guo, YC Chen, WT Chen, LH Young, KJ Lee, ZY Wu, PJ Tsai
Science of the Total Environment 636, 1139-1148, 2018
milr: Multiple-Instance Logistic Regression with Lasso Penalty.
PY Chen, CC Chen, CH Yang, SM Chang, KJ Lee
R J. 9 (1), 446, 2017
BSGS: Bayesian Sparse Group Selection.
KJ Lee, RB Chen
R Journal 7 (2), 2015
On the determinants of the 2008 financial crisis: A Bayesian approach to the selection of groups and variables
RB Chen, YC Chen, CH Chu, KJ Lee
Studies in Nonlinear Dynamics & Econometrics 21 (5), 2017
Of needles and haystacks: revisiting growth determinants by robust Bayesian variable selection
KJ Lee, YC Chen
Empirical Economics 54 (4), 1517-1547, 2018
Bayesian variable selection in a finite mixture of linear mixed-effects models
KJ Lee, RB Chen
Journal of Statistical Computation and Simulation 89 (13), 2434-2453, 2019
Spatial Bayesian hierarchical model with variable selection to fMRI data
KJ Lee, S Hsieh, T Wen
Spatial Statistics 21, 96-113, 2017
Variable selection in finite mixture of regression models with an unknown number of components
KJ Lee, M Feldkircher, YC Chen
Computational Statistics & Data Analysis 158, 107180, 2021
Determination of correlations in multivariate longitudinal data with modified Cholesky and hypersphere decomposition using Bayesian variable selection approach
KJ Lee, RB Chen, MS Kwak, K Lee
Statistics in Medicine 40 (4), 978-997, 2021
Cerebral control of winking before and after learning: An event‐related fMRI study
CCK Lin, KJ Lee, CH Huang, YN Sun
Brain and behavior 9 (12), e01483, 2019
Application of Spatial Bayesian Hierarchical Models to fMRI Data
KJ Lee
Assessment of Cellular and Organ Function and Dysfunction using Direct and …, 2016
Finite mixture of regression modeling for exchange market pressures during the financial crisis: A robust Bayesian approach to variable selection
KJ Lee, YC Chen
2016 臺灣計量經濟學會年會, 2016
Bayesian approaches to variable selection with highly correlated regressors:: Predictive perspectives from cross-country growth regressions
KJ Lee, YC Chen
總體經濟計量模型研討會, 2015
Supplemental Material for Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging Time-Series Data
KJ Lee, GL Jones, BS Caffo, S Bassett
An Application of Bayesian Hierarchical Linear Modeling to an Event-Related fMRI Study in Inhibition Control
KJ Lee, P Stewart, M Tivarus
Bayesian Robust Generalized Mixed Models for Longitudinal Data
KJ Lee, RB Chen, K Lee, C Kim
An Application of Spatial Bayesian Variable Selection to fMRI Time-Series Data
KJ Lee, G Jones
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