Publications

Books (* indicates first or corresponding author)

1. Lee, K.*, Lee, K., Kim, Y. and Kim, E. (2021). 스토리가 있는 통계학(Korean version of 'What is a p-value anyway?'). Shinhan Press Media. 

2. Lee, K.* (2022). Longitudinal data analysis: using R (경시적자료분석 R활용). Freedom Academy.  오타수정 

Submitted Papers (* indicates first or corresponding author)

1. Lee, J., Jang, E. J. and Lee, K.*  (2025). Analysis of longitudinal zero-inflated count data using overall marginalized hurdle models. Submitted.

2. Lee, K.*,  Jang, E. J. and Dey, D. (2025). Overall marginalized models for longitudinal zero-inflated count data. Submitted.

3. Lee, K.-J., Joo, J., Chen, R.-B., Shyr, Y. and Lee, K.* (2025).  Bayesian joint modeling of longitudinal and survival data with heteroscedastic covariance structures for repeated outcomes. Submitted.

SCI(E) Journal  (* indicates first or corresponding author)

1. Lee, K.*, Choi, J., Jang, E. J. and Dey, D. (2025). Multivariate robust linear models for multivariate longitudinal data. Journal of Multivariate Analysis. 206, March 2025, 105392. 

2. Jang, E. J., Rhee, A., Cho, S.-K., and Lee, K.*(2025). Analysis of longitudinal lupus data using multivariate t-linear models.  Statistics in Medicine. 44, issue 1-2, e10248.

3. Lee, K.-J., Chen, R.-B. and Lee, K.* (2025). Robust Bayesian cumulative probit linear mixed models for longitudinal ordinal data. Computational Statistics. 40,  441-468

4. Lee, S., Lee, K., Park, J.-H., Kyung, M., Yun, S.-T., Lee, J., and Joo, Y. (2024). Clustering of temporal profiles in US climate change data using logistic mixture of spatial multivariate linear models. Stochastic Environmental Research and Risk Assessment. 38, 3719-3733..

5. Lee, K.-J., Kim, C., Yoo, J. K. and Lee, K.* (2024). Multivariate probit linear mixed models for multivariate longitudinal binary data. Statistics in Medicine. 43, issue 8, 1527-1548.  

6. Lee, K.*, Koo, D. and Kim, C. (2023).  A Bayesian method for multinomial probit model. Journal of the Korean Statistical Society. 52, issue 1, 265 - 281.

7. Lee, K.-J., Kim, C., Chen, R.-B. and Lee, K.* (2022). Robust probit linear mixed models for longitudinal binary data. Biometrical Journal, 64, issue 7, 1307-1324..

8. Rhee, A., Kwak, M.-S. and Lee, K.* (2022). Robust modeling of multivariate longitudinal data using modified Cholesky and hypersphere decompositions. Computational Statistics & Data Analysis,  June, 170, Article 107439.

9. Lee, K.*, Lee, C.-H., Kwak, M.-S. and Jang, E. (2021). Analysis of multivariate longitudinal data using ARMA Cholesky and hypersphere decompositions. Computational Statistics & Data Analysis, April, 156, Article 107144.

10. Lee, K.-J., Chen, R.-B., Kwak, M.-S. and Lee, K.* (2021). Determination of correlations in multivariate longitudinal data with modified Cholesky and hypersphere decomposition using Bayesian variable selection approach. Statistics in Medicine, February, 40, issue 4, 978-997.

11. Lee, K.*, Cho, H., Kwak, M.-S and Jang, E. (2020). Estimation of covariance matrix of multivariate longitudinal data using modified Choleksky and hypersphere decompositions. Biometrics, March, 76, issue 1, 75-86.

12. Kim, J., Sohn, I. and Lee, K.* (2020). Bayesian cumulative logit random effects models with ARMA random effects covariance matrix. Journal of the Korean Statistical Society, January, 49, issue 1, 32-54.

13. Lee, K.*, Choi, Y., Um, H.Y. and Yoo, J.K. (2019) On Fused Dimension Reduction in Multivariate Regression. Chemometrics and Intelligent Laboratory Systems, October, 193, Article 103828.

14. Lee, K.*, Jung, H., and Yoo, J. K. (2019). Modeling of the ARMA random effects covariance matrix in logistic random effects models. Statistical Methods & Applications, June, 28, 281-299.

15. Lee, K.* and Joo, Y. (2019). Marginalized models for longitudinal count data. Computational Statistics & Data Analysis, August, 136, 47-58.

16. Lee, K.*, Baek, C., and Daniels, M. J. (2017). ARMA Cholesky factor models for the covariance matrix of linear models. Computational Statistics & Data Analysis, November, 115, 267-280.

17. Lee, K.*, Song, H., and Yoo, J. (2017). Dimension test approach of heteroscedasticity in linear model. Communications in Statistics - Simulation and Computation, June, 46, 4356-4366.

18. Lee, K.*, Sohn, I., and Kim, D. (2016). Analysis of long series of longitudinal ordinal data.  Computational Statistics & Data Analysis, February, 94, 363-371.

19. Lee, K. * and Yoo, J. (2014). Bayesian Cholesky factor models in random effects covariance matrix for  generalized linear mixed models. Computational Statistics & Data Analysis, 80, 111-116.

20. Lee, M., Lee, K.*, and Lee, J. (2014). Marginalized transition shared random effects models for  longitudinal binary data with nonignorable dropout. Biometrical Journal, 56, 230-242.

21. Lee, K.* and Yoo, J. (2014). Canonical correlation analysis through linear modeling.  Australian & New Zealand Journal of Statistics, 56, 59-72.

22. Kang, S., Lee, K.*, and Lee, W. (2014). Noninformative priors for the generalized half-normal distribution. Journal of the Korean Statistical Society, 43, 19-29.

23. Lee, K.* and Daniels, M. (2013). Causal inference for bivariate longitudinal quality of life data in presence of death using global odds ratios. Statistics in Medicine, 32, 4275-4284.

24. Lee, K.*, Daniels, M., and Joo, Y. (2013). Flexible marginalized models for bivariate longitudinal ordinal data. Biostatistics, 14, 462-476.

25. Lee, K.*, Yoo, J. K., Lee, J., and Hagan, J. (2012). Modeling the random effects covariance matrix for the generalized linear mixed models. Computational Statistics & Data Analysis, 56, 1545-1551.

26. Liu, X., Wang, K., and Lee, K. (2011). Association of standardized estimated glomerular filtration rate with the prevalence of hypertension among adults in the United States. Journal of Human Hypertension, 25, 469-475.

27. Yoo, J. K. and Lee, K.* (2011). Model-free predictor tests in survival regression through sufficient dimension reduction. Lifetime Data Analysis, 17, 433-444.

28. Lee, K.*, Kang, S., Liu, X., and Seo, D. (2011). Likelihood-based approach for analysis of longitudinal nominal data using marginalized random effects models. Journal of Applied Statistics, 38, 1577-1590.

29. Lee, K.*, Joo, Y., Song, J. J. and Harper D. (2011). Analysis of zero-inflated clustered count data: A marginalized model approach. Computational Statistics & Data Analysis, 55, 824-837.

30. Lee, K.*, Daniels, M., and Sargent, D. (2010). Causal effects of treatments for informative missing data due to progression/death. Journal of the American Statistical Association, 105, 912-929.

31. Lee, K.* and Mercante, D. (2010). Longitudinal nominal data analysis using marginalized models. Computational Statistics & Data Analysis, 54, 208--218.

32. Yoo, J. K., Lee, K., and Wu, S. (2010). On the extension of sliced average variance estimation to multivariate  regression. Statistical Methods and Applications, 19, 529-540.

33. Joo, Y., Brumback, B., Lee, K., Yun, S., Kim, K., and Joo, C. (2009). Clustering of temporal profiles using  Bayesian logistic mixture model: application to groundwater level data to understand recharge characteristics  of urban groundwater. Journal of Agricultural, Biological, and Environmental Statistics, 14, 356-373.

34. Lee, K.*, Joo, Y., Yoo, J. K., and Lee, J. (2009). Marginalized random effects models for multivariate longitudinal   binary data. Statistics in Medicine, 28, 1284-1300.

35. Joo, Y., Kim, D., Lee, K., Yun, S.,Kim, K., and Mercante, D. (2009). Estimation of anthropogenic pollution  using a Bayesian contamination model:  an application to fractured bedrock groundwater from Han river  watershed, South Korea. Environmetrics, 20, 221-234.

36. Lee, K.* and Daniels, M. (2008). Marginalized models for longitudinal ordinal data with application to quality   of life studies. Statistics in Medicine, 27, 4359-4380.

37. Lee, K.* and Daniels, M. (2007). A class of Markov models for longitudinal ordinal data. Biometrics, 63, 1060-1067.

38. Joo, Y., Lee, K., Min, J., Yun, S., and Park, T. (2007). Logistic mixture of multivariate regressions for analysis of water quality impacted by agrochemicals. Environmetrics, 18, 499-514.

39. Joo, Y., Casella, G., Booth, J., Lee, K., and Enkemann, S. (2007). Normalization of dye bias in microarray data using the mixture of splines model. Statistical Applications in Genetics and Molecular Biology, 6, Issue 1, Article 2.

Domestic Journal (* indicates first or corresponding author)

1. Jeong, Y. and Lee, K.* (2025).  Analysis of post-retirement happiness using labor panel data: a Bayesian cumulative probit mixed model approach.  Journal of the Korean Data & Information Science Society. 36(6), 973-986.

2. Kim, B. and Lee, K.* (2025). Analysis of medical panel data using multivariate probit linear mixed models.  Journal of the Korean Data & Information Science Society,  36(3), 471–487.

3. Kim, D. and Lee, K.* (2025). Analysis of credit ratings of Korean corporate bonds using cumulative logit random effects models. The Korean Journal of Applied Statistics. 38 (2), 169–188.

4. Kwon, M. and Lee, K.*(2024). Workspace panel survey data analysis using Bayesian cumulative probit linear mixed model. The Korean Journal of Applied Statistics.  37 (6), 783-799. 

5. Oh, C. and Lee, K.* (2024). Analysis of medical panel binary data using marginalized models. The Korean Journal of Applied Statistics,  37(4), 467-484.

6. Lee, J. and Lee, K.* (2024). Comparison study of multivariate linear models for multivariate longitudinal data. Journal of the Korean Data & Information Science Society. 35 (1), 33-45.

7. Kwon, Y. and  Lee, K.* (2023). Bayesian marginalized two-part mixed effects model based on generalized gamma distribution. The Korean Journal of Applied Statistics. 36 (3), 225-243.

8. Kim, M. and Lee, K.* (2023). Analysis of 2016 household financial welfare survey data using probit models. Journal of the Korean Data & Information Science Society. 34 (3), 443-458.

9.  Gong, H. G. and Lee, K.* (2023). Comparison study of methods on modeling covariance matrix in longitudinal data. Journal of the Korean Data & Information Science Society, 34 (2), 255-277.

10. Lee, I. and Lee, K.* (2022). KCYP panel data analysis using Bayesian multivariate linear model.  The Korean Journal of  Applied Statistics, December, 35(6), 703-724.

11.  Park, J. and Lee, K.* (2022). Analysis of Korean Longitudinal Study of Ageing using Bayesian Robust Probit Linear Mixed Model. Journal of the Korean Data & Information Science Society, July, 33 (4), 657-676.

12. Kim, Y. and Lee, K.* (2022).  Comparison study for Bayesian multivariate linear model. Journal of the Korean Data & Information Science Society, March, 33 (2), 249-268.

13. Koo, D. and Lee, K.* (2022). Analysis of tax finance panel data using multivariate t- linear models.  Journal of the Korean Data & Information Science Society, January, 33 (1), 11-34.

14. Yun, D. and Lee, K.* (2020).  Comparison between AR and ARMA covariance matrices for multivariate longitudinal data. Journal of the Korean Data & Information Science Society. 31 (5), 721-740.

15. Suh, R. and Lee, K.* (2020). Analysis of labor panel data using multivariate regression models. Journal of the Korean Data & Information Science Society, 31 (4), 549-568.

16. Kwak, N.Y. and Lee, K.* (2020). Comparison study of modeling covariance matrix for multivariate longitudinal data. The Korean Journal of Applied Statistics, 33, No. 3, 1-16.

17. Kim, J. and Lee, K.* (2020). Bayesian baseline-category logit random effects model for longitudinal nominal data. Communications for Statistical Applications and Methods, 27 (2), 201-210.

18. Lee, K.* (2019). Marginalized models for longitudinal ordinal data with nonignorable dropout. Journal of the Korean Data & Information Science Society, 30, 479-490.

19. Kang, D., Kim, B.O. and Lee, K.* (2018). Marginalized random effects models with ARMA random effects covariance matrix. Journal of the Korean Data & Information Science Society, 29, 501-512.

20. Sung, Y. and Lee, K.* (2018). Negative binomial loglinear mixed models with general random effects covariance matrix. Communications for Statistical Applications and Methods, 25, 61-70.

21. Choi, J. and Lee, K.* (2017). Poisson linear mixed models with ARMA random effects covariance matrix. Journal of the Korean Data and Information Science Society, 28, 659-668.

22. Nam, S. and Lee, K.* (2017). Comparison of the covariance matrix for general linear model. The Korean Journal of Applied Statistics, 30, 103-117.

23. Kim, J., Sohn, I., and Lee, K.* (2017). Bayesian modeling of random effects precision/covariance matrix in cumulative logit random effects models. Communications for Statistical Applications and Methods, 24, 81-96.

24. Han, E.-J. and Lee, K.* (2016). Dynamic linear mixed models with ARMA covariance matrix. Communications for Statistical Applications and Methods, 23, 575-585.

25. Lee, K.* and Kim, S. (2016). Modeling of random effects covariance matrix in marginalized random effects models. Journal of the Korean Data and Information Science Society, 27, 815-825.

26. Kyung, Y. and Lee, K.* (2015). Bayesian pattern mixture model for longitudinary binary data with nonignorable missingness. Communications for Statistical Applications and Methods, 22, 589-598.

27. Kim, J. and Lee, K.* (2015). Survey of models for random effects covariance matrix in generalized linear mixed model. The Korean Journal of Applied Statistics, 28, 211-219.

28. Jin, I. and Lee, K.* (2014). Hurdle model for longitudinal zero-inflated count data analysis. The Korean Journal of Applied Statistics, 27, 923-932.

29. Jeon, J. and Lee, K.* (2014).  Review and discussion of marginalized random effects models. Journal of the Korean Data & Information Science Society, 25, 1263-1272.

30. Lee, K.* and Sung. S. (2014).  Autoregressive Cholesky factor modeling for marginalized random effects models. Communications for Statistical Applications and Methods, 21, 169-181.

31. Lee, K.* (2013). Bayesian modeling of random effects covariance matrix for generalized linear mixed models. Communications for Statistical Applications and Methods, 20, 235-240.

32. Joo, Y., Lee, K., and Jung, H. (2008). Estimation of interval censored regression spline model with variance function. Journal of the Korean Data & Information Science Society, 19, 1247-1253.

33. Lee, I., Kim, D., and Lee, K. (1999). Bayesian methods for combining results from different experiments. The Korean Communications in Statistics, 6, 181-191.