국립부경대학교 | Statistics & Data Science
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Ildo Ha Professor

  • Address : 
  • Email : mail
  • Tel : 051-629-5536
  • HomaPage : http://

 Education:  

Seoul National University, Dept. of Statistics (Ph.D.,1999. 02)

Professional Experience: 

Korea Military Academy, Dept. of Mathematics, Full-time lector

Deagu Haany University, Dept. of Data Management, Professor

Univ. of Limerick (Ireland), Centre of Biostatistics, Visiting professor


Research Interests: 

Multivariate survival analysis

Medical Statistics

Machine learning for medical and time-to-event data

Development of likelihood-based algorithm 


There is no registered information.

 

Honors and Award:

Research award of The Society of Korean Medicine (2004)

Research award of The Korean Data & Information Science Society (2009)

Best research award in field of Statistics of The Korean Federation of Science and Technology Societies (2013)


Administrative and Editorial work:

Fellow of The Royal Statistical Society (2006 - present)

Co-Editor of the Korean Journal of Applied Statistics (2014 - 2016)

Associate Editor of Computational Statistics (2008-2012)

Associate Editor of Journal of the Japanese Society of Computational Statistics (2009 -present)


English Book:

Ha, I.D., Jeong J.-H. and Lee, Y. (2017). 

Statistical Modelling of Survival Data with Random Effects. Springer


Development of software: 

Ha, I. D., Noh, M., Kim, J. and Lee, Y. (2018). frailtyHL: frailty models via h-likelihood. R-package version 2.1.

http://cran.r-project.org/package=frailtyHL


Selected International publications:

1. Ha, I.D., Lee, Y. and Song, J. (2001). Hierarchical likelihood approach   for frailty models. Biometrika, 88, 233-243.

2. 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.

3. 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.

4. Ha, I.D., Lee, Y. and MacKenzie, G. (2007). Model selection for multi-component frailty models. Statistics in Medicine, 26, 4790-4807.

5. 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.

6. Christian, N. J., Ha, I. D. and Jeong, J. (2016). Hierarchical likelihood inference on clustered competing risks data. Statistics in Medicine, 35, 251-267.

7. Park, E and Ha, I. D. (2019). Penalized variable selection for accelerated failure time models with random effects. Statistics in Medicine, 38, 878-892.

8. Kwon, S., Ha, I. D. and Kim, J.-M. (2020). Penalized variable selection in copula survival models for clustered time-to-event data. Journal of Statistical Computation and Simulation, 90, 657-675.

9. 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, 25, 109-133.

10. Chee, 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, Published online.


There is no registered information.