Peer Reviewed Journals

1. Huang, Y., Kim, H. J., Huang, C-Y., and Kim, M-O. (in press), Bayesian random-effects meta-analysis integrating individual participant data and aggregate data, Journal of the American Statistical Association
2. Hwang, Y., Kim, H. J., Chan, W., Hong, C., and MacEachern, S. N. (2025), Bayesian model calibration and sensitivity analysis for oscillating biological experiments, Technometrics, 67, 333-343
3. Ahn, S., Ji, E., Ohn, J.H., Jo, H., Park, M-J., Kim, H. J., and Shin, C.H. (in press), Evaluating the utility of data integration with synthetic data and statistical matching, Scientific Reports
4. Lee, S., Chae, S. J., Jang, I-H., Oh, S-C., Kim, S.-M., Lee, S. Y., Kim, J. H., Ko, J., Kim, H. J., Song, I-C., Kim, J. K., and Kim, T-D. (2024), B7H6 is the predominant activating ligand driving natural killer cell-mediated killing in patients with liquid tumours: evidence from clinical, in silico, in vitro, and in vivo studies, eBioMedicine, 110, 105459
5. Park, M-J., Kim, H. J., and Kwon, S. (2024), Disseminating massive frequency tables by masking aggregated cell frequencies, Journal of the Korean Statistical Society, 53, 328-348
6. Hong, H., Cortez, M. J., Cheng, Y-Y, Kim, H. J., Choi, B., Josic, K., and Kim, J. K. (2023), Inferring delays in partially observed gene regulation processes, Bioinformatics, 39
7. Wang, Z., Kim, H. J., and Kim, J. K. (2023), Survey data integration for regression analysis using model calibration, Survey Methodology, 49, 89-115
8. Zang, H., Kim, H. J.*, Huang, B., and Szczesniak, R. (2023), Bayesian causal inference for observational studies with missingness in covariates and outcomes, Biometrics, 79, 3624-3636
9. Chen, C., Bin, H., Kouril, M., Liu, J., Kim, H. J., and Sivaganesan, S. (2023), An application programming interface implementing Bayesian approaches for evaluating effect of time-varying treatment with R and Python, Frontiers in Computer Science, 5:1183380
10. Deng, Q., Nam, J. H., Yilmaz, A. S., Chang, W., Pietrzak, M., Li, L., Kim, H. J.*, and Chung, D.* (2023), graph-GPA 2.0: Improving multi-disease genetic analysis with integration of functional annotation data, Frontiers in Genetics, 14:1079198
11. Allen, C., Chang, Y., Neelon, B., Chang, W., Kim, H. J., Li, Z., Ma, Q. and Chung, D. (2022), A Bayesian multivariate mixture model for high throughput spatial transcriptomics, Biometrics, 79, 1775-1787
12. Thompson, K. J. and Kim, H. J. (2022), Incorporating economic conditions in synthetic microdata for business programs, Journal of Survey Statistics and Methodology, 10, 830–859
13. Hu, J., Drechsler, J. and Kim, H. J. (2022), Accuracy gains from privacy amplification through sampling for differential privacy, Journal of Survey Statistics and Methodology, 10, 688-719
14. Khatiwada, A.,Wolf, B. J., Yilmaz, A. S., Ramos, P. S., Pietrzak, M., Lawson, A., Hunt, K. J., Kim, H. J., and Chung, D. (2022), GPA-Tree: Statistical approach for functional-annotation-tree-guided prioritization of GWAS results, Bioinformatics, 38, 1067-1074
15. Kim, H. J.*, Drechsler, J., and Thompson, K. J. (2021), Synthetic microdata for establishment surveys under informative sampling, Journal of the Royal Statistical Society, Series A, 184, 255-281
16. Jung, Y., MacEachern, S. N., and Kim, H. J. (2021), Modified check loss for efficient estimation via model selection in quantile regression, Journal of Applied Statistics, 48, 866-886
17. Hwang, Y., Kim, H. J.*, Chang, W., Yeo, K., and Kim, Y. (2019), Bayesian pollution source identification via an inverse physics model, Computational Statistics and Data Analysis, 134, 76-92
18. Kim, H. J.*, Lu, B., Nehus, E. J., and Kim, M-O.* (2019), Estimating heterogeneous treatment effects for latent subgroups in observational studies, Statistics in Medicine, 38, 339–353
19. Liu, X., Chen, A., Caicedo-Casso, A., Cui, G., Du, M., He, Q., Lim, S., Kim, H. J., Hong, C., and Liu, Y. (2019), FRQ-CK1 interaction determines the period of circadian rhythms in Neurospora, Nature Communications, 10, 4352
20. Kim, H. J., Yu, Z., Lawson, A., Zhao H., and Chung, D. (2018), Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA, Bioinformatics, 34, 2139-2141
21. Kim, H. J.*, Reiter, J. P., and Karr, A. F. (2018), Simultaneous edit-imputation and disclosure limitation for business establishment data, Journal of Applied Statistics, 45, 63-82
22. Kortemeier, E., Ramos, P. S., Hunt, K. J., Kim, H. J., Hardiman G., Chung D. (2018), ShinyGPA: An interactive visualization toolkit for investigating pleiotropic architecture using GWAS datasets, PLoS ONE, 13, (1): e0190949
23. Chung, D., Kim, H. J., and Zhao, H. (2017), graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture, PLoS Computational Biology, 13, (2): e1005388
24. Kim, H. J.*, Cox, L. H., Karr, A. F., Reiter, J. P., and Wang, Q. (2015), Simultaneous edit-imputation for continuous microdata, Journal of the American Statistical Association, 110, 987-999
25. Kim, H. J.* and MacEachern, S. N. (2015), The generalized multiset sampler, Journal of Computational and Graphical Statistics, 24, 1134-1154
26. Kim, H. J.*, Karr, A. F. and Reiter, J. P. (2015), Statistical disclosure limitation in the presence of edit rules, Journal of Official Statistics, 31, 121-138
27. Kim, H. J.*, Reiter, J. P., Wang, Q., Cox, L. H. and Karr, A. F. (2014), Multiple imputation of missing or faulty values under linear constraints, Journal of Business and Economics Statistics, 32, 375-386

Book Chapters, Technical Reports, or Non-English-Language Journal Articles

28. An, S., Doan, T., Lee, J., Kim, J., Kim, Y. J., Kim, Y., Yoon, C., Jung, S., Kim, D., Kwon, S., Kim, H. J., Ahn, J., and Park, C. (2023, written in Korean), A comparison of synthetic data approaches using utility and disclosure risk measures (유용성과 노출 위험성 지표를 이용한 재현자료 기법 비교 연구), Korean Journal of Applied Statistics, 36, 141–166
29. Abowd, J. A., Benedetto, G. L., Garfinkel, S. L., Dahl, S. A., Dajani, A. N., Graham, M., Hawes, M. B., Karwa, V., Kifer, D., Kim, H. J., Leclerc, P., Machanavajjhala, A., Reiter, J. P., Rodriguez, R., Schmutte, I. M., Sexton, W. N., Singer, P. E., and Vilhuber, L. (2020), The Modernization of Statistical Disclosure Limitation at the U.S. Census Bureau, Census Working Papers, U.S. Census Bureau, Washington, DC
30. Thompson, K. J., Kim, H. J., Bassel, N., Bembridge, K., Coleman, C., Freiman, M., Garcia, M., Kaputa, S., Riesz, S., Singer, P., Valentine, E, White, K. T., and Whitehead, D. (2020), Final Report: Economic Census Synthetic Data Project Research Team, ADEP Working Paper Series ADEP-WP-2020-05, U.S. Census Bureau, Washington, DC
31. Park, M-J. and Kim, H. J.* (2016, written in Korean), Statistical disclosure control for public microdata: present and future (마이크로데이터 공표를 위한 통계적 노출제어 방법론 고찰) , Korean Journal of Applied Statistics, 29, 1041–1059
32. Kim, H. J. and Karr, A. F. (2013), The effect of statistical disclosure limitation on parameter estimation for a finite population, Technical Report 183, National Institute of Statistical Sciences, Durham, NC
33. Bakshi, B. R., Kim, H. J., and Goel, P. K. (2011), Using thermodynamics and statistics to improve the quality of life-cycle inventory data Thermodynamics and the Destruction of Resources, in Thermodynamics and the Destruction of Resources, pp. 235-248, Cambridge University Press

Softwares

• clusterMI (ver 1.2.1) by Vincent Audigier and Hang J. Kim
  : Allows clustering of incomplete observations by addressing missing values using multiple imputation. Four multiple imputation methods are proposed, two are based on joint modelling and two are fully sequential methods.
• synMicrodata (ver 2.0.0) by Hang J. Kim, Juhee Lee, Young-Min Kim, and Jared Murray
  : Synthetic microdata generator based on a non-parametric Bayesian model for mixed data type (continuous and categorical variables) where missing values exist.
• GGPA (ver 1.16.0) by Dongjun Chung, Hang J. Kim, and Carter Allen
  : A graphical model for prioritizing GWAS results and investigating pleiotropic architecture, introduced in Chung et al. (2017, PLoS Comput. Biol.) and Kim et al. (2018, Bioinformatics)
• DPImputeCont (ver 1.2.2) by Hang Kim
  : Imputation engine for continuous data using a Dirichlet process Gaussian mixture model, introduced in Kim et al. (2014, JBES)