교수
Seoul National University, Dept. of Statistics (Ph.D.,1999. 02)
| 2014.09 - 현재 | 국립부경대학교 통계·데이터사이언스전공 교수 |
| 2014.05 - 현재 | 의료기기 임상통계 전문가(식약처 식품의약품안전평가원) |
| 2008 - 2012 | Associate Editor of Computational Statistics |
| 2006.01 - 현재 | Fellow of The Royal Statistical Statistics (영국왕립통계학회) |
| 2006.09 - 2007.08 | 연구방문교수 (University of Limerick, Centre for Biostatistics, 아일랜드) |
| 2003.04 - 2014.08 | 대구한의대학교 제한동의학술원 의학통계부장, 임상시험심사위원(IRB; 2005 - 2009) |
| 1996.09 - 2014.08 | 대구한의대학교 통계학과/정보과학부/데이터경영학과 전임강사/조교수/부교수/교수 |
| 1993.07 - 1994.07 | 육군사관학교 교수부 수학과 교관(전임강사) |
| · Multivariate survival analysis |
| · Random effects survival models(frailty models and competing risks models) |
| · H-likelihood Inference and Hierarchical generalized linear models(HGLMs) |
| · Machine learning (penalized variable selection) |
| · Medical statistics using randomized clinical trial |
등록된 내용이 없습니다.
1. 논문
| 1 | Ha, I.D., Lee, Y. and Song, J. (2001). Hierarchical likelihood approach for frailty models. Biometrika, 88, 233-243. |
| 2 | Ha, I.D., Lee, Y. and Song, J. (2002). Hierarchical likelihood approach for mixed linear models with censored data. Lifetime Data Analysis, 8, 163-176. |
| 3 | Ha, I.D., Park, T. and Lee, Y. (2003). Joint modelling of repeated measures and survival time data. Biometrical Journal, 45, 647-658. |
| 4 | Ha, I.D. and Lee, Y. (2003). Estimating frailty models via Poisson hierarchical generalized linear models. Journal of Computational and Graphical Statistics, 12, 663-681. |
| 5 | Ha, I. D. and Lee, Y. (2005). Comparison of hierarchical likelihood versus orthodox best linear unbiased predictor approaches for frailty models. Biometrika, 92, 717-723. |
| 6 | Ha, I.D. and Lee, Y. (2005). Multilevel Mixed Linear Models for Survival Data. Lifetime Data Analysis, 11, 131-142. |
| 7 | Noh, M., Ha, I.D. and Lee, Y.(2006). Dispersion frailty models and HGLMs. Statistics in Medicine, 25, 1341-1354. |
| 8 | Ha, I. D. (2006). Discussion of Lee and Nelder’s paper. Journal of Royal Statistical Society, C, 55, 176. |
| 9 | Lee, H.-S., Seo, J.-C. and Ha, I.D. (2006). Acupuncture for smoking cessation?: commentary. Yonsei Medical Journal, 47, 155-156. |
| 10 | Ha, I.D., Lee, Y. and Pawitan, Y. (2007). Genetic mixed liner models for twin survival data. Behavior Genetics, 37, 621-630. |
| 11 | Ha, I.D., Lee, Y. and MacKenzie, G. (2007). Model selection for multi-component frailty models. Statistics in Medicine, 26, 4790-4807. |
| 12 | Ha, I. D. (2007). Discussion of Zeng and Lin’s paper. Journal of Royal Statistical Society, B, 69, 549-550. |
| 13 | Ha, I. D., Noh, M. and Lee, Y. (2010). Bias reduction of likelihood estimators in semiparametric frailty models. Scandinavian Journal of Statistics, 37, 307-320. |
| 14 | Ha, I. D. and MacKenzie, G. (2010). Robust frailty modelling using non-proportional hazards models. Statistical Modelling, 10, 315-332. |
| 15 | Lee, Y. and Ha, I. D. (2010). Orthodox BLUP versus h-likelihood methods for inferences about random effects in Tweedie mixed models. Statistics and Computing, 20, 295-303. |
| 16 | Ha, I. D., Sylvester, R., Legrand, C. and MacKenzie, G. (2011). Frailty modelling for survival data from multi-centre clinical trial. Statistics in Medicine, 30, 2144-2159. |
| 17 | Ha, I. D., Noh, M. and Lee, Y. (2012). frailtyHL: A package for fitting frailty models with h-likelihood. R Journal, 4, 28-37. |
| 18 | Ha, I. D., Pan, J., Oh, S. and Lee, Y. (2014). Variable selection in general frailty models using penalized h-likelihood. Journal of Computational and Graphical Statistics, 23, 1044-1060. |
| 19 | Ha, I. D., Lee, M., Oh, S., Jeong, J.-H., Sylvester, R. and Lee, Y. (2014). Variable selection in subdistribution hazard frailty models with competing risks data. Statistics in Medicine, 33, 4590-4604. |
| 20 | Paik, M. C., Lee, Y. and Ha, I. D. (2015). Frequentist inference on random effects based on summarizability. Statistica Sinica, 25, 1107-1132. |
| 21 | Ha, I. D., Vaida, F. and Lee, Y. (2016). Interval estimation of random effects in proportional hazards models with frailties. Statistical Methods in Medical Research. 25, 936-953. |
| 22 | Ha, I. D., Christian, N. J., Jeong, J.-H., Park, J. and Lee, Y. (2016). Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties. Statistical Methods in Medical Research, 25, 2488-2505. |
| 23 | Christian, N. J., Ha, I. D. and Jeong, J. (2016). Hierarchical likelihood inference on clustered competing risks data. Statistics in Medicine, 35, 251-267. |
| 24 | Lee, M., Ha, I. D. and Lee, Y. (2017). Frailty modeling for clustered competing risks data with missing cause of failure. Statistical Methods in Medical Research, 26, 356-373 |
| 25 | Ha, I. D., Noh, M. and Lee, Y. (2017). H-likelihood approach for joint modelling of longitudinal outcomes and time-to-event data. Biometrical Journal, 59, 1122-1143. |
| 26 | Hong, S.W., Suh, Y.S., Kim,D.H., Kim,M.K., Kim,H.S., Park,K.S., Hwang, J. S. Shin,S.J., Cho,C.H., Jung, S.W., Ha, I. D. and Kwon, Y.K. (2018). Manifestations of Sasang typology according to common chronic diseases in Koreans. Evidence-Based Complementary and Alternative Medicine, 1-8. |
| 27 | Park, E. and Ha, I. D. (2019). Penalized variable selection for accelerated failure time models with random effects. Statistics in Medicine, 38, 878-892. |
| 28 | Huang, R., Xiang, L. Ha,I .D. (2019). Frailty proportional mean residual life regression for clustered survival data: A hierarchical quasi-likelihood method. Statistics in Medicine. 38, 4854？4870. |
| 29 | Ha, I .D., Kim,J.-M. and Emura,T. (2019) Profile likelihood approaches for semiparametric copula and frailty models for clustered survival data, Journal of Applied Statistics, 46, 2553-2571. |
| 30 | Emura,T. Shih, J.-H. Ha, I .D. and Wilke, R.F. (2020). Comparison of the marginal hazard model and the sub-distribution hazard model for competing risks under an assumed copula, Statistical Methods in Medical Research, 29, 2307-2327. |
| 31 | Ha, I. D., Xiang, L., Peng, M. Jeong, J.-H. and Lee, Y. (2020). Frailty modelling approaches for semi-competing risks data. Lifetime Data Analysis, 26, 109-133. |
| 32 | Kim, J.-M., Li, C. and Ha, I. D. (2020). Machine learning techniques applied to US army and navy data. International Journal of Productivity and Quality Management, 29, 149-166. |
| 33 | Chee1, C.-S. Ha ,I.D., Seo,B. and Lee,Y. (2021). Semiparametric estimation for nonparametric frailty models using nonparametric maximum likelihood approach, Statistical Methods in Medical Research, 30, 2485-2502. |
| 34 | Rakhmawati1, T.W., Ha,I.D., Lee,H. and Lee,Y. (2021). Penalized variable selection for cause-specific hazard frailty models with clustered competing-risks data, Statistics in Medicine, 40, 6541-6557. |
| 35 | Ha, I.D. and Lee,Y. (2021). A review of h？likelihood for survival analysis, Japanese Journal of Statistics and Data Science, 4, 1157-1178. |
| 36 | Hao, L., Kim, J., Kwon, S. and Ha, I.D. (2021). Deep learning-based survival analysis for high-dimensional survival data. Mathematics, 9, 1244, 359-366 |
| 37 | Kwon, S. Ha, I.D. , Shih, J.-H. and Emura,T. (2022). Flexible parametric copula modeling approaches for clustered survival data, Pharmaceutical Statistics, 21, 69-88. |
| 38 | Kim, J.-K. and Ha, I.D. (2022) Deep learning-based residual control chart for count data, Quality Engineering, 34, 370-381. |
| 39 | Jaouimaa, F.-Z., Ha, I.D. and Burke, K. (2023). Penalized variable selection in multi-parameter regression survival modeling, Statistical Methods in Medical Research, 32, 2455-2471. |
| 40 | Kim, J., Ha, I.D. , Kwon, S., Jang, I. and Na, M.-H. (2023). A smart farm DNN survival model considering tomato farm effect, Agriculture, 13, 1782. |
| 41 | Kim, J., Jeong, B., Ha, I.D. et al. (2024). Bias reduction for semi-competing risks frailty model with rare events: application to a chronic kidney disease cohort study in South Korea, Lifetime Data Analysis. 30, 310-326. |
| 42 | Jaouimaa, F.-Z., Ha, I.D. and Burke, K. (2024). Multi-parameter regression survival modelling with random effects, Statistical Modelling, 24, 245-265. |
| 43 | Lin, H., Ha, I.D. , Jeong, J.-H. and Lee, Y. (2024). Joint AFT random-effect modeling approach for clustered competing-risks data, Journal of Statistical Computation and Simulation. 94, 2114-2142. |
| 44 | Seo, B., Ha, I.D. (2024). Semiparametric accelerated failure time models under unspecified random effect distributions, Computational Statistics and Data Analysis, 195, 1-19. |
| 45 | Ha, I.D. (2024). A Study on the Relationship Between Deep Learning and Statistical Models, Measurement: Interdisciplinary Research and Perspectives, 22, 188-199. |
| 46 | Kim, J.-M., Kim, S. and Ha, I.D. (2024). Copula deep learning control chart for multivariate zero inflated count response variables, Statistics, Online published. |
| 47 | Lee, H., Ha, I.D. , Hwang, C. and Lee, Y. (2023). Subject-specific deep neural networks for count data with high-cardinality categorical features. arXiv:2310.11654v1, https://doi.org/10.48550/arXiv.2310.11654 |
| 48 | Lee, H., Ha, I.D. , Hwang, C. and Lee, Y. (2023). Deep neural networks for semiparametric frailty models via h-likelihood. arXiv:2307.06581v1, https://doi.org/10.48550/arXiv.2307.06581 |
2. 소프트웨어
| 1 | Ha, I. D., Noh, M. and Lee, Y. (2012). frailtyHL: frailty models via h-likelihood. R-package version 1.1. |
| 2 | Ha, I. D., Noh, M., Kim, J. and Lee, Y. (2019). frailtyHL: frailty models via h-likelihood. R-package version 2.3. http://cran.r-project.org/package=frailtyHL. |