Kuo-Jung Lee

Professor, Department of Statistics and Institute of Data Science, National Cheng Kung University 

Director, Institutional Research Division, National Cheng Kung University


Education: University of Minnesota -- Twin Cities

Research: Bayesian Statistics, fMRI Data Analysis, Spatio-Temporal Models

Publications

1.         Multivariate Probit Linear Mixed Models for Multivariate Longitudinal Binary Data.Kuo-Jung Lee, Chanmin Kim, Jae Keun Yoo, and Keunbaik Lee.  Statistics in Medicine, Online. February, 2024.

2.         DDNAS: Discretized Differentiable Neural Architecture Search for Text Classification. Transactions on Intelligent Systems and Technology. Kuan-Chun Chen, Cheng-Te Li, Kuo-Jung Lee. ACM Transactions on Intelligent Systems and Technology, 15, 5, pp. 1–22, 2023

3.         Use of spatial panel-data models to investigate factors related to incidence of end-stage renal disease: A nationwide longitudinal study in Taiwan. Chien-Chou Su, Kuo-Jung Lee, Chi-Tai Yen, Lu-Hsuan Wu, Chien-Huei Huang, Meng-Zhan Lu & Ching-Lan Cheng. BMC Public Health 23, 247 (2023). https://doi.org/10.1186/s12889-023-15189-7

4.         Robust probit linear mixed models for longitudinal binary data. Kuo-Jung Lee, Chanmin Kim, Ray-Bing Chen, Keunbaik Lee. Biometrical Journal, 1307-1324, 2022

5.         Variable selection in finite mixture of regression models with an unknown number of components.  Lee, K-J., Chen Y-C., & Feldkircher M. Computational Statistics and Data Analysis, 158, June 2021, 107180

6.         Determination of Correlations in Multivariate Longitudinal Data with Modified Cholesky and Hypersphere Decomposition using Bayesian Variable Selection Approach. Lee K-J., Chen, R-B., & Lee KB.  Statistics in Medicine, 978-997, 2021

7.         Bayesian variable selection in a finite mixture of linear mixed-effects models. Lee, K-J. & Chen, R-B., 2019 九月 2, 於 : Journal of Statistical Computation and Simulation. 89, 13, p. 2434-2453.

8.         Cerebral control of winking before and after learning: An event-related fMRI study. Lin, C. C. K., Lee, K. J., Huang, C. H. & Sun, Y. N., 2019 十二月 1, 於 : Brain and Behavior. 9, 12, e01483.

9.         An instantaneous spatiotemporal model for predicting traffic-related ultrafine particle concentration through mobile noise measurements. Lin, M. Y., Guo, Y. X., Chen, Y. C., Chen, W. T., Young, L. H., Lee, K. J., Wu, Z. Y. & Tsai, P. J., 2018 九月 15, 於 : Science of the Total Environment. 636, p. 1139-1148.

10.         Of Needles and Haystacks: Revisiting Growth Determinants by Robust Bayesian Variable Selection. Lee, K. J. & Chen, Y. C., 2018 六月 1, 於 : Empirical Economics. 54, 4, p. 1517-1547 31 p 

11.         Milr: Multiple-instance logistic regression with lasso penalty. Chen, P. Y., Chen, C. C., Yang, C. H., Chang, S-M. & Lee, K-J., 2017 六月 1, 於 : R Journal. 9, 1, p. 446-457.

12.       On the Determinants of the 2008 Financial Crisis: A Bayesian Approach to the Selection of Groups and Variables.  Chen, R. B., Chen, Y. C., Chu, C. H. & Lee, K. J., 2017 十二月 20, 於 : Studies in Nonlinear Dynamics and Econometrics. 21, 5, 20160107.

13.       Spatial Bayesian hierarchical model with variable selection to fMRI data. Lee, K. J., Hsieh, S. & Wen, T., 2017 八月, 於 : Spatial Statistics. 21, p. 96-113.

14.       Bayesian variable selection for finite mixture model of linear regressions. Lee, K. J., Chen, R. B. & Wu, Y. N., 2016 三月 1, 於 : Computational Statistics and Data Analysis. 95, p. 1-16.

15.       BSGS: Bayesian sparse group selection. Lee, K-J. & Chen, R-B., 2015 一月 1, 於 : R Journal. 7, 2, p. 122-133.

16.       Spatial Bayesian variable selection models on functional magnetic resonance imaging time-series data. Lee, K. J., Jones, G. L., Caffo, B. S. & Bassett, S. S., 2014 一月 1, 於 : Bayesian Analysis. 9, 3, p. 699-732.

 

17.       Bayesian analysis of Box-Cox transformed linear mixed models with ARMA(p, q) dependence. Lee, J. C., Lin, T. I., Lee, K. J. & Hsu, Y. L., 2005 八月 1, 於 : Journal of Statistical Planning and Inference. 133, 2, p. 435-451.

 

Teaching