고려대학교 정경대학 통계학과

학력

• Ph.D. in Statistics, 2005, Carnegie Mellon University, Pittsburgh, PA, U.S.A.
  M.S. in Statistics, 2000, Seoul National University, Seoul, Korea 
  B.S. in Computer Science and Statistics, 1998, Seoul National University, Seoul, Korea 

경력 및 수상

Sep 2015 ~ present, Professor of Statistics, Korea University
Sep 2010 ~ Aug 2015, Associate professor of Statistics, Korea University
Sep 2009 ~ Aug 2010, Assistant professor of Statistics, Korea University
Mar 2008 ~ Aug 2009, Assistant professor of Statistics, Inha University
Aug 2005 ~ Feb 2008, Assistant professor of Statistics, University of Maryland, Baltimore County 

2020.08.~ 2021.07 고려대학교 통계연구소장

2018.08.~ 2020.07 고려대학교 통계학과장

2019 제14회 한국통계학회 학술진흥상 수상

2017 고려대학교 석탑강의상 수상 (통계계산방법)

2014 Science and Technology Excellent Paper Award, The Korea Federation of Science and Technology Societies

2011 Best Paper Award in the Journal of Nonparametric Statistics, awarded at Joint Statistical Meeting

2006 Honorable mention in Leonard J. Savage Award : Theory & Methods, awarded at Eighth World Meeting of ISBA

 


Editorial Service
2023.01 - present. Associate Editor, Journal of Korean Statistical Society
2020.01 - 2022.12. Co-Editor, Journal of Korean Statistical Society
2017.01 – 2019.12. Managing Editor, Journal of Korean Statistical Society
2013.10 – present. Associate Editor, Computational Statistics and Data Analysis
2013.09 – 2021.12. Associate Editor, Korean Journal of Applied Statistics
2012.03 – 2014.12. Associate Editor, STAT

단행본

• • Choi, T. (2020). Essentials of Probability for Statistics, 자유아카데미. 
  • Huh, M-H. and Choi, T. (2013). Calculus Essential for Statistics, 교우사. 
  • Shi, J. Q. and Choi, T. (2011). Gaussian Process Regression Analysis for Functional Data, Chapman & Hall/CRC Press 

북챕터

• • Choi, T., and Lenk, P. (2019). Bayesian spectral analysis regression, invited chapter in Flexible Bayesian regression modeling, Academic Press, 221-249.
  
  

연구논문

• 국제학술지  
  
  
  • Kobayashi, G., Sugasawa, S., Kawakubo, Y., Han, D., and Choi, T. (2024). Predicting COVID-19 hospitalisation using a mixture of Bayesian predictive syntheses." Annals of Applied Statistics. 18 (4) 3383 - 3404. 
  
  • Lenk, P., Lee, J., Han, D., Park, J. and Choi, T. (2024) Hierarchical Bayesian spectral regression with shape constraints for multi-group data. Computational Statistics & Data Analysis.vol 200, 108036.
  
  • Han, D., Lim, D., and Choi, T. (2023) Bayesian sparse seemingly unrelated regressions model with variable selection and covariance estimation via the horseshoe+. Journal of the Korean Statistical Society: vol 52, Issue 3, 676-714.
  
  • Yang, D., Choi, T., Lavigne, E. and Chung, Y.(2022) Non-parametric Bayesian covariate-dependent multivariate functional clustering: An application to time-series data for multiple air pollutants. Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol 71, Issue 5, 1521-1542 
  
  • Kobayashi, G., Roh, T., Lee, J., and Choi, T. (2021). Flexible Bayesian quantile curve fitting with shape restrictions under the Dirichlet process mixture of the generalized asymmetric Laplace distribution, Canadian Journal of Statistics, Vol 49, Issue 3, 698-730.
  
  • Jo, S., Park, B., Chung, Y., Kim, J., Lee, E., Lee, J., and Choi, T. (2021). Bayesian semiparametric mixed effects models for meta-analysis of the literature data : An application to cadmium toxicity studies, Statistics in Medicine, Vol 40, Issue 16, 3762-3778.
  
  • Yu, J., Park, J., Choi, T., Hashizume, M., Kim, Y., Honda, Y., and Chung, Y. (2021). Nonparametric Bayesian functional meta-regression : Applications in Environmetal Epidemiology, Journal of Agricultural, Biological and Environmental Statistics, Vol 26, 45-70.
  
  • Park, J., Choi, T., and Chung, Y. (2021). Nonparametric Bayesian functional two-part random effects model for longitudinal semicontinuous data analysis, Biometrical Journal, Vol 63, 787-805.
  
  • Lim, D., Park, B., Nott, D. J., Wang, X. and Choi, T. (2020). Sparse signal shrinkage and outlier detection in high-dimensional quantile regression with variational Bayes, Statistics and Its Interface, Vol 13, No. 2, 237-249.
  
  • Jo, S., Choi, T., Park, B., and Lenk, P. (2019). bsamGP: An R package for Bayesian spectral analysis models using Gaussian process priors, Journal of Statistical Software, Volume 90, Number 10, 1-41. 
  
  • Kim, H., Roh, T., and Choi, T. (2019). Bayesian analysis of semiparametric Bernstein polynomial regression models for data with sample selection, Statistics: A Journal of Theoretial and Applied Statistics, Volume 53, No. 5, 1082-1111.
  
  • Ong, V.M.H., Nott, D. J, Choi, T., Jasra, A. (2019). Flexible online multivariate regression with variational Bayes and the matrix-variate Dirichlet process, Foundations of Data Science, Volume 1, Number 2, 129-156. 
  
  • Kim, G., and Choi, T. (2019). Asymptotic properties of nonparametric estimation and quantile regression in Bayesian structural equation models, Journal of Multivariate Analysis, Volume 171, 68-82.
  
  • Yang, J., Cox, D. D., Lee, S., Ren, P. and Choi, T. (2017). Efficient Bayesian hierarchical functional data analysis with basis function approximation using Gaussian-Wishart processese. Biometrics, Volum 73, Issue 4, 1082-1091.
  
  • Ong, V.M.H., Mensha, D. K., Nott, D.J., Jo, S., Park, B. and Choi, T. (2017). A variational Bayes approach to a semiparametric regression using Gaussian process priors. Electronic Journal of Statistics, Volum 11, Number 2, 4258-4296.
   
  • Hart, J. and Choi, T. (2017). Nonparametric goodness of fit via cross-validation Bayes factors. Bayesian Analysis, Volume 12, 653-677.
  
  • Kim, G., Kim, Y., and Choi, T. (2017). Bayesian analysis of the proportional hazards model with time-varying coefficients. Scandinavian Journal of Statistics, Volume 44, 524-544.
  
  • Lenk, P. J. and Choi, T. (2017). Bayesian analysis of shape-restricted functions using Gaussian process priors. Statistica Sinica, Volume 27, 46-69.
  
  • Choi, T. Kim, H-J., and Jo, S. (2016). Bayesian variable selection approach to a Bernstein polynomial regression model with stochastic constraints. Journal of Applied Statistics, Volume 43, 2751-2771.
  
  • Lian, H., Choi, T., Meng, J., and Jo, S. (2016). Posterior convergence for Bayesian functional linear regression.  Journal of Multivariate Analysis, Volume 150, 27-41.
  
  • Yang, J., Zhu, H., Choi, T., and Cox, D. (2016). Smoothing and mean-covariance estimation of functional data with a Bayesian hierarchical model. Bayesian Analysis, Volume 11, 649-670.      
               
  • Jo, S., Roh, T. and Choi, T. (2016). Bayesian spectral analysis models for quantile regression with Dirichlet process mixtures.  Journal of Nonparametric Statistics, Volume 28, 177-206. 
  
  • Kim, H-J., Choi, T., and Lee, S. (2016). A hierarchical Bayesian regression model for the uncertain functional  constraint using screened scale mixtures of Gaussian distributions. Statistics : A Journal of Theoretical and Applied Statistics, Volume 50, 350-376.
               
  • Hart, J., Choi, T., and Yi, S. (2016). Frequentist nonparametric goodness-of-fit tests via marginal likelihood ratio, Computational Statistics and Data Analysis, Volume 96, 120-132.
  
  • Kim, H-J., Choi, T., and Jo, S. (2016). Bayesian factor analysis with uncertain functional constraints about factor loadings, Journal of Multivariate Analysis, Volume 144, 110-128.
  
  • Choi, T. and Rousseau, J. (2015). A note on Bayes factor consistency in partial linear models, Journal of Statistical Planning and Inference, Volume 166, 158-170.
  
  • Choi, T. and Woo, Y. (2015). A partially linear Model using a Gaussian process prior, Communications in Statistics, Simulation and Computation, Volume 44, Issue 7, 1770-1786.
  
  • Kim, H. and Choi, T. (2014). On Bayesian estimation of regression models subject to uncertainty about functional constraints, Journal of the Korean Statistical Society, Volume 43, Issue 1, 133-147.
      
  • Choi, T. and Woo, Y. (2013). On asymptotic properties of Bayesian partially linear models,  Journal of the Korean Statistical Society, Volume 42, Issue 4, 529-541.
  
  • Yi, G., Shi, J. Q. and Choi, T. (2011) "Penalized Gaussian process regression and classification for high-dimensional nonlinear data", Biometrics, Volume 67, Issue 4, 1285-1294.
  
  • Yi, S. and Choi, T. (2011) "A direct approach to understanding posterior consistency of Bayesian regression problems" Communications in Statistics, Theory and Methods, Volume 40, Issue 18, 3316-3326.
  
  • Kim, J.-M., Jung, Y.-S., Choi, T., and Sunger, E. A. (2011) "Partial correlation with Copula modeling" Computational Statistics and Data Analysis, Volume 55, Issue 3, 1357-1366.
  
  • Kim, J-M, Sungur, E. A., Choi, T., and Heo, T.-Y. (2011) "Generalized Bivariate Copulas and Their Properties", Model Assisted Statistics and Applications-International Journal; Vol. 6, 127-136.
  
  • Choi, T., Shi, J. Q., and Wang, B. (2011) "A Gaussian process regression approach to a single-index model", Journal of Nonparametric Statistics, vol.23, No. 1, 21-36.
  
  • Choi, T., Schervish, M. J., Schmitt, A. K., and Small, M. J. (2010) "Bayesian Hierarchical Analysis for Multiple Health Endpoints in a Toxicity Study", Journal of Agricultural, Biological and Environmental Statistics, vol. 15, No. 3, 290-307.
  
  • Choi, T. (2009) "Asymptotic properties of posterior distributions in nonparametric regression with non-Gaussian errors", Annals of the Institute of Statistical Mathematics, vol. 61, No. 4, pp 835-859.
  
  • Choi, T., Lee, J., and Roy, A. (2009), "A note on the Bayes factor in a semiparametric regression model", Journal of Multivariate Analysis, vol. 100, Issue 6, pp 1316-1327.
  
  • Choi, T. (2008) "Convergence of posterior distribution in the mixture of regressions", Journal of Nonparametric Statistics, vol. 20, Issue 4, pp 337-351.
  
  • Choi, T. and Ramamoorthi, R. V. (2008) "Remarks on consistency of posterior distributions", IMS Collections, vol. 3, Pushing the Limits of Contemporary Statistics : Contributions in Honor of Jayanta K. Ghosh, pp 170-186.
  
  • Choi, T., Schervish, M. J., Schmitt, A. K., and Small, M. J. (2008) "A Bayesian approach to logistic regression model with incomplete information", Biometrics, vol. 64, Issue 2, pp 424-430.
  
  • Choi, T., Schervish, M. J. (2007) "On posterior consistency in nonparametric problems", Journal of Multivariate Analysis, vol. 98, Issue 10, pp 1969-1987.
  
  • Choi, T. (2007) "Alternative posterior consistency results in nonparametric binary regression using Gaussian process priors", Journal of Statistical Planning and Inference, vol. 137, 2979-2983.
  
  • Choi, K., Kim, D., and Choi, T. (2006) "Estimating the number of clusters using a multivariate location test statistics", Springer Lecture note in Computer Science (LNCS-LNAI 4223), Fuzzy Systems and Knowledge Discovery, 373-382.
  
  
  국내학술지
  
  
  • 이장원, 한돈구, 박지찬, 최태련 (2024). 베이지안 분위수 확률적 변경 모형에 대한 변분 베이즈 방법, Journal of the Korean Data & Information Science  Society, 35(2), 239-257.
  
  • 김기성, 최태련 (2020). 허들 및 영과잉 회귀모형의 베이지안 변화점 식별 모형 : 과학화 전투훈련의 전투단계 변화 분석, Journal of the Korean Data & Information Science  Society, 31(6), 1089-1107.
  
  • Lee, D., Lee, E., Jo, S., and Choi, T. (2020). Bayesian ordinal probit semiparametric regression models :  KNHANES 2016 data analysis of the relationship between smoking behavior and coffee intake, The Korean Journal of Applied Statistics, 33(1), 25-46.
  
  • 김현아, 노태영, 최태련 (2018). 해밀턴 필터를 이용한 베이지안 마코프-스위칭 ARMA(p,q)-GARCH(r,s) 모형 연구, Journal of the Korean Data Analysis Society, 20(4), 1801-1817.
  
  • Jo, S., Seok, I. and Choi, T. (2016). A nonparametric Bayesian seemingly unrelated regression model, The Korean Journal of Applied Statistics, 29(4), 627-641.
  
  • 노태영, 최태련 (2016). 벡터자기회귀 모형 추정을 위한 베이지안 축소 방법론 비교 연구, Journal of the Korean Data Analysis Society, 18(4), 1857-1870.
  
  • 김효태, 조성일, 최태련 (2015). 적응 기각 추출을 기반으로 하는 난수 생성기의 성능비교, Journal of the Korean Data & Information Science Society, 26(3), 593-610.
  
  • 조미형, 최태련, 이령화 (2014). 레버리지 효과를 포함하는 베이지안 확률 변동성 모형의 실증적 비교 분석, Journal of the Korean Data Analysis Society, 16(2), 703-717.
  
  • Lee, M., Choi, T., Kim, J. and Woo, H. (2013). Bayesian analysis of dose-effect relationship of Cadmium for benchmark dose evaluation, The Korean Journal of Applied Statistics, 26(3), 453-470.
  
  • 오유경, 최태련, 조미형 (2012). 편차정보기준을 이용한 베이지안 확률변동성 모형 선택에 관한 실증적 연구, Journal of the Korean Data Analysis Society, 14(4), 1871-1888.
        
  • Woo, Y., Choi, T, and Kim W. (2012). A comparison study on the performance of Bayesian partially linear models, 한국통계학회논문집, 19(6), 885-898.
  
  • 이지호, 최태련, 우윤성 (2011) "영과잉포아송 모형에 대한 베이지안 방법 연구", 응용통계연구 제 24권, 4호, 677-693. 
  
  • 최태련 (2010) "계층적 베이지안 방법을 이용한 용량반응연구에서의 기준용량 추정", Journal of the Korean Data Analysis Society, Vol. 12, No.1, pp119-133