Publication

Students are underlined. ＊ is the article what I am an corresponding author

  1. Jin, I.H. and Liang, F. (2013) Fitting social network models using varying truncation stochastic approximation MCMC algorithms. Journal of Compu-
    tational and Graphical Statistics     . Vol. 22. No. 4: pp. 927-952. Selected JCGS highlights at the Interface 2012: Future of Statistical Computing.
  2. Liang, F. and Jin, I.H. (2013) A Monte Carlo Metropolis-Hasting algorithms for sampling from distributions with intractable normalizing constants. Neural Computation, Vol. 25. No. 8: pp. 2199-2234.
  3. Jin, I.H., Yuan, Y., and Liang, F. (2013) Bayesian analysis for exponential random graph models using the adaptive exchange sampler. Statistics and Its Interface, Vol. 6: pp. 559-576.
  4. Jin, I.H. and Liang, F. (2014) Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants. Computational Statistics and Data Analysis. Vol. 71: pp. 402-416.
  5. Jin, I.H., Liu, S., Thall, P. F., and Yuan, Y. (2014) Using data augmentation to facilitate conduct of phase I/II clinical trials with delayed outcomes. Journal
    of the American Statistical Association     . Vol. 109. No. 506: pp. 525-536.
  6. Jin, I.H., Huo, L., Yin, G., and Yuan, Y. (2015) Phase I trial design for drug combinations with Bayesian model averaging. Pharmaceutical Statistics, Vol.
    14. No. 2: pp. 109-119
  7. Liang, F., Jin, I.H., Song, Q, and J.S. Liu. (2016) An adaptive exchange algorithm for sampling from distribution with intractable normalizing constants. Journal of the American Statistical Association. Vol. 111. No. 513: pp. 377-393.
  8. Jin, I.H., Yuan, Y., and Bandyopadhyay, D. (2016) A Bayesian hierarchical spatial model for dental caries assessments using non-gaussian Markov random fields. The Annals of Applied Statistics. Vol. 10. No. 2: pp. 884-905.
  9. Liu, H., Jin, I.H. and, Zhang, Z. (2018) Structural Equation Modeling of Social Networks: Specification, Estimation, and Applications. Multivariate Behavioral Research, Vol. 53. No. 5: pp.714-730. Awarded Tanaka Award: Most Outstanding Article in Multivariate Behavioral Research Volume 53.
  10. Jin, I.H.＊ and Jeon, M. (2019) A doubly latent space joint model for local item and person dependence in item response analysis. Psychometrika, Vol. 84. No. 1: pp. 236-260.
  11. Nam, J. H., Yun, J., Jin, I.H.＊, and Chung, D.＊(2020) hubViz: A Novel Tool for Hub-centric Visualization. Chemometrics and Intelligent Laboratory Systems . Vol. 203. 104071.
  12. Yun, J., Shin, M., Jin, I.H.＊, and Liang, F. (2020) Stochastic approximation Hamiltonian Monte Carlo. Journal of Statistical Computation and Simulation. Vol. 90. No. 17: pp. 3135-3156.
  13. Che, C., Jin, I.H., and Zhang, Z. (2021) Network Mediation Analysis Using Model-based Eigenvalue Decomposition. Structural Equation Modeling: A Multidisciplinary Journal. Vol. 28. No. 1: pp. 148-161.
  14. Liu, H., Jin, I.H., Zhang, Z, and Yuan, Y. (2021) Social Network Mediation Analysis: Latent Space Approach. Psychometrika. Vol. 86. No. 1: pp. 272-298.
  15. Jeon, M., Jin, I.H., Schweinberger, M., and Baugh, S. (2021) Mapping unobserved item-respondent interactions: A latent space item response model with interaction map. Psychometrika. Vol. 86. No. 2: pp. 378-403.
  16. Y. Zhang, S. Cao, C. Zhang, Jin, I.H., and Zang, Y. (2021) A Bayesian Adaptive Phase I/II Clinical Trial Design with Late-onset Competing Risk Outcomes. Biometrics. Vol. 77. Issue 3: pp. 796-808.
  17. Park, J., Jin, I.H.＊, and Schweinberger, M. (2022) Bayesian Model Selection for High-Dimensional Ising Models, with Applications to Educational Data.
    Computational Statistics and Data Analysis. Vol. 125: Article 107325.
  18. Park, J., Jeon, Y., Shin, M., Jeon, M., and Jin, I.H.＊(2022) Bayesian Shrinkage for Functional Network Models, with Applications to Longitudinal Item Response Data. Journal of Computational and Graphical Statistics. Vol. 31. No. 2: pp 360-377.
  19. Liu, F., Eugenio, E., Jin, I.H., and Bowen, C. M. (2022) Differentially Private Synthesis of Social Network Structure via Exponential Random Graph Model. Journal of Survey Statistics & Methodology. Vol. 10. No. 2: pp. 753-784
  20. D. Ko, M. Jeon, S. Lee, Jin, I.H.＊, and Park. H＊. (2022) Analyzing differences between parent- and self-report measures with a latent space approach. Plos One. Vol. 17. No. 6. Article e0269376.
  21. Jin, I.H., Jeon, M., Schweinberger, M, Yun, J., and Lin. L. (2022) Multilevel network item response modelling for discovering differences between innovation and regular school systems in Korea. Journal of Royal Statistical Society, Series C. Vol 71. Issue 5: pp. 1225-1244.
  22. Kim, H., Jeon, Y., Kim, H., Kim, D., Park, S., Jin, I.H., and Jung, S.J. (2022) Application of joint latent space item response model to clustering stressful life
    events and Beck Depression Inventory II: Results from Korean epidemiological survey data. Epidemiology and Health. Vol. 44. Article e2022093
  23. D. Ko, Im, J., and Jin, I.H. (2022) Quantile Regression with Multiple Proxy Variables. Stat. ​e547.
  24. Park, J., Jin, I.H.＊, and Jeon, M. (2023) How social network influences human behavior: An integrated latent space approach for differential social influence. Psychometrika. In Press. DOI: 10.1007/s11336-023-09934-5.
  25. Jin, I.H.＊, Yun, J., Kim, H., and Jeon, M. (2023) A Latent Space Accumulator Model for Response Time: Applications to Cognitive Assessment Data. Stat. Accepted. ArXiv:2203.14306.

Submitted Manuscripts

  1. Park, J., Kang, S. and Jin, I.H. (2023) Control of Frequentist Type I Error Rates in Hierarchical Linear Models for Multiregional Clinical Trials Using a Bayesian Approach. Submitted to Communication in Statistics - Simulation and Computation.
  2. Park, J., Hu, W., Jin, I.H.＊, Liu, H. and Zang, Y. (2022) A Bayesian Precision Response-adaptive Phase II Clinical Trial Design for Radiotherapies with Competing Risk Survival Outcomes. Revision Submitted to Statistical Methods in Medical Research. ArXiv:2203.06830.
  3. Jeon, Y., Park, J., Jin, I.H.＊, and Chung, D. (2021) Network-based Trajectory Topic Interaction Map for Text Mining of COVID-19 Biomedical Literature. Submitted to Journal of Applied Statistics. ArXiv:2106.07374.
  4. Go, D., Park, J., Kim, G., Park, J., Jeon, M., and Jin, I.H.＊(2022) LSIRM: An R Package for a Latent Space Item Response Model with an Interaction Map. Submitted to R Journal. ArXiv:2205.06989.
  5. Yi, S., Kim, M., Park, J., Jeon, M., and Jin, I.H.＊ (2023) Impacts of Innovation School System in Korea: A Latent Space Item Response Model with Neyman-Scott Point Process. Submitted to Journal of the Royal Statistical Socieity, Series A.

Refereed Conference Proceeding

  1. Liu, F., Eugenio, E., Jin, I.H., Bowen, C. M. (2020) Differentially Private Generation of Social Networks via Exponential Random Graph Models, Proceedings of 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). pp. 1695-1700.

Unpublished Manuscript

  1. Jin, I.H. and Liang, F. (2009) Bayesian analysis for exponential random graph models using the double Metropolis-Hastings sampler. Technical Report 2009-097. Institute for Applied Mathematics and Computer Science, Texas A&M University
  2. Jin, I.H., Liu, F., Eugenio, E., Kim, J., and Liu, S. (2018) Bayesian Hierarchical Spatial Model for Small Area Estimation with Non-ignorable Non-responses and Its Applications to the NHANES Dental Caries Assessments. ArXiv:1810.05297.
  3. Brodersen, A., Jin, I.H., Cheng, Y., and Jeon, M. (2021) Applying the Network Item Response Model to Student Assessment Data. ArXiv:2003.07657.
  4. You, K., Kim, I., Jeon, M., and Jin, I.H.＊ (2022) Multiple Latent Spaces Comparisons Using the Topological Analysis. ArXiv:2208.12435.
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