Peer Reviewed Journals

* corresponding author
1. Wang, Z., Kim, H. J., and Kim, J. K. (in press), Survey data integration for regression analysis using model calibration, Survey Methodology
2. 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
3. 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
4. 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
5. 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
6. 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           (previoues version: RRS2019-07, U.S. Census Bureau)
7. 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
8. 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
9. 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
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. Kim, H. J.* and MacEachern, S. N. (2015), The generalized multiset sampler, Journal of Computational and Graphical Statistics, 24, 1134-1154
17. 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
18. 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, and Articles Written in Korean

• An et al. (2023, written in Korean), A comparison of synthetic data approaches using utility and disclosure risk measures, Korean Journal of Applied Statistics, 36, 141–166
• 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
• 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
• 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
• 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
• 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

• GGPA (ver 1.0.0)
  : 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)
• EditImputeCont (ver 1.1.4)
  : Editing-imputation engine for continuous microdata under linear edit rules using a Dirichlet process Gaussian mixture model, introduced in Kim et al. (2015, JASA)
• DPImputeCont (ver 1.2.2)
  : Imputation engine for continuous data using a Dirichlet process Gaussian mixture model, introduced in Kim et al. (2014, JBES)