Publications (by Research Topic)

For publications organized in reverse chronological order, please visit this page.

^† corresponding author    * PhD advisee

Causal Inference and Meta Analysis

- Semiparametric Bayesian Modeling

1. 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
2. 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
3. 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
4. Huang, Y., Kim, H. J., Huang, C-Y., and Kim, M-O.^† (2025), Bayesian random-effects meta-analysis integrating individual participant data and aggregate data, Journal of the American Statistical Association, 120, 2128-2139

Quantitative Biomedical Sciences

- Statistical Genomics; Spatial Transcriptomics; Circadian Rhythm Modeling; Graphical Modeling

5. 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
6. 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
7. Kortemeier, E., Ramos, P. S., Hunt, K. J., Kim, H. J., Hardiman G., and Chung D.^† (2018), ShinyGPA: An interactive visualization toolkit for investigating pleiotropic architecture using GWAS datasets, PLoS ONE, 13, (1): e0190949
8. 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
9. 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
10. 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
11. GGPA (ver 1.16.0) y Chung, D., Kim, H. J., and Allen, C.
12. 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
13. 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
14. 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
15. Lim, Y., Kim, H. J., and Hwang, B. S.^† (2025), Nonparametric Bayesian latent class model for longitudinal zero-inflated count data. Journal of Nonparametric Statistics
16. Hwang, Y.^†, Kim, H. J., Chang, W., Hong, C., and MacEachern, S. N. (2025), Bayesian model calibration and sensitivity analysis for oscillating biological experiments, Technometrics, 67, 333-343

Semiparameteric Bayesian Modeling and Statistical Computing

- Uncertainty Quantification; Computer Experiment; Generalized Multiset Sampler

17. 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

18. Kim, H. J.^† and MacEachern, S. N. (2015), The generalized multiset sampler, Journal of Computational and Graphical Statistics, 24, 1134-1154
19. 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
20. 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
21. Chang, W., Hwang, Y., and Kim, H. J.^† (2025), Physics-driven dynamic interpolation with application to pollution satellite images, Spatial Statistics, 69, 100923
22. Kim, H. J.^†, MacEachern, S. N., Kim, Y. M., and Jung, Y. (2026), Kernel density estimation with a Markov chain Monte Carlo sample, Computational Statistics and Data Analysis, 214, 108271

Data Quality and Privacy of Official Data

- Synthetic Microdata; Multiple Imputation; Data Editing; Survey Sampling; Variance Estimation

23. 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

24. 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
25. DPImputeCont (ver 1.2.2) by Kim, H. J.
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.^†, 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
28. Park, M-J. and Kim, H. J.^† (2016, written in Korean), Statistical disclosure control for public microdata: present and future, Korean Journal of Applied Statistics, 29, 1041–1059

    (마이크로데이터 공표를 위한 통계적 노출제어 방법론 고찰)

29. 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
30. 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

31. 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

32. 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
33. 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
34. 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
35. 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, Korean Journal of Applied Statistics, 36, 141–166

    (유용성과 노출 위험성 지표를 이용한 재현자료 기법 비교 연구)

36. Wang, Z., Kim, H. J., and Kim, J. K.^† (2023), Survey data integration for regression analysis using model calibration, Survey Methodology, 49, 89-115
37. 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
38. clusterMI (ver 1.2.1) by Audigier, A. and Kim, H. J.
39. synMicrodata (ver 2.0.0) by Kim, H. J., Lee J., Kim, Y-M., and Murray, J.
40. Kim, H. J., Rotemberg, M., and White, T. K. (2025)

    Manufacturing Dispersion: How Data Cleaning Choices Affect Measured Misallocation and Productivity Growth in the Annual Survey of Manufactures,

    Census Working Papers, CES-25-67, U.S. Census Bureau, Washington, DC

41. Ji, E., Ohn, J. H., Jo, H., Park, M-J., Kim, H. J., Shin, C. M., and Ahn, S. (2025)

    Evaluating the utility of data integration with synthetic data and statistical matching

    Scientific Reports, 15:19627

42. Lee, J., Kim, H. J., Murray, J. S., and Kim, Y. M.^† (in press), synMicrodata: An R package for generating synthetic microdata via a nonparametric Bayesian approach, SoftwareX