Statistical Learning

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Welcome to Jong-June Jeon's homepage!

Deep Ranking and Model Selection Criteria

Information

Name Jong-June Jeon
Work Associate Professor, Department of Statistics, University of Seoul
Office Mirae Building 7th floor #712
email jj.jeon@uos.ac.kr
jj.jeon@gmail.com
Github /github.com/jenjong
phone/fax 82-2-6490-2637 / 82-2-6490-2629
Mailing address Department of Statistics, University of Seoul, 163 Siripdae-ro, Dongdaemun-gu, Seoul, Korea, 02504


Education

  • Seoul National University, Seoul, Korea (Ph.D. in Statistics, 2012)
  • Seoul National University, Seoul, Korea (B.S. in Business Administrations, 2005)


Works

Studies of statistical theory and methology

  • Rank-Consistency of the generalized Bradkey-Terry model with link misspecification, (summitted), 2017
    data file (description)
  • Homogeneity detection for the high-dimensional generalized linear model, JJ Jeon, S Kwon, H Choi, Computational Statistics & Data Analysis 114, 61-74, 2017
  • The sparse Luce model, JJ Jeon, H Choi, Applied Intelligence, 1-12, 2017
  • Consistent model selection criteria for quadratically supported risks, Y Kim, JJ Jeon, The Annals of Statistics 44 (6), 2467-2496, 2016.
  • A Necessary Condition for the Strong Oracle Property Y Kim, JJ Jeon, S Han, Scandinavian Journal of Statistics, 2015
  • Revisiting the Bradley-Terry model and its application to information retrieval, JJ Jeon, Y Kim, Journal of the Korean Data and Information Science Society 24 (5), 1089-1099, 2013

Statistics for Climatology

  • Application of distribution-free nonstationary regional frequency analysis based on L-moments, Sung, J., Y.-O. Kim, JJ Jeon, Theoretical and Applied Climatology, 2017 (DOI: 10.1007/s00704-017-2249-8).
  • Abrupt change point detection of annual maximum precipitation using fused lasso, JJ Jeon, JH Sung, ES Chung, Journal of Hydrology 538, 831-841, 2016.
  • Bayesian analysis to detect abrupt changes in extreme hydrological processes, S Jo, G Kim, JJ Jeon, Journal of Hydrology 538, 63-70, 2016
  • Expected probability weighted moment estimator for censored flood data JJ Jeon, YO Kim, Y Kim Advances in Water Resources 34 (8), 933-945, 2011
  • Artificial neural network model for optimizing operation of a seawater reverse osmosis desalination plant YG Lee, YS Lee, JJ Jeon, S Lee, DR Yang, IS Kim, JH Kim Desalination 247 (1-3), 180-189, 2009

Statistics for radilogy

  • Low-dose (2-mSv) computed tomography for suspected appendicitis: Applicability in an emergency department, American Journal of emergency medicine, (doi:10.1016/j.ajem.2018.03.031)
  • Can We Perform CT of the Appendix with Less Than 1 mSv? A De-escalating Dose-simulation Study, Park, J.,J. Jeon, S. Lee, A. C. Dhanatwari, J. Sim, H. Kim, K. Lee, European Radiology, (DOI10.1007/s00330-017-5159-3)
  • Systematic review and meta-analysis for CT features of complicated appendicitis, Kim H., J. Park , Y. Lee, S. Lee, J. Jeon, K. Lee., Radiology, 2017 (DOI: 10.1148/radiol.2017171260)
  • Portfolio credit risk model with extremal dependence of defaults and random recovery, JJ Jeon, S. Kim, Y. Kim, Journal of credit risk, 2017, 13(2), 1-32.
  • The Correlations of Parameters Using Contrast Enhanced Ultrasonography in the Evaluation of Prostate Cancer Angiogenesis,s SI Hwang, HJ Lee, KJ Kim, JH Chung, HS Jung, JJ Jeon, Journal of Korean Society of Ultrasound in Medicine 32 (2), 132-142, 2013
  • A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability, SM Kim, H Han, JM Park, YJ Choi, HS Yoon, JH Sohn, MH Baek, YN Kim, JJ Jeon, Journal of digital imaging 25 (5), 599-606, 2012
  • Evaluation of Tumor Angiogenesis in a Mouse PC‐3 Prostate Cancer Model Using Dynamic Contrast‐Enhanced Sonography HJ Lee, SI Hwang, JH Chung, JJ Jeon, JH Choi, HS Jung Journal of Ultrasound in Medicine 31 (8), 1223-1231, 2012
  • Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information, KJ Kim, B Kim, H Lee, H Choi, JJ Jeon, JH Ahn, KH Lee Medical physics 38 (12), 6449-6457, 2011
  • Artificial neural network model for optimizing operation of a seawater reverse osmosis desalination plant YG Lee, YS Lee, JJ Jeon, S Lee, DR Yang, IS Kim, JH Kim Desalination 247 (1-3), 180-189, 2009

Statistics for finance and engineering

  • Portfolio credit risk model with extremal dependence of defaults and random recovery, JJ Jeon, S. Kim, Y. Kim, Journal of credit risk, 2017, 13(2), 1-32.
  • Artificial neural network model for optimizing operation of a seawater reverse osmosis desalination plant YG Lee, YS Lee, JJ Jeon, S Lee, DR Yang, IS Kim, JH Kim Desalination 247 (1-3), 180-189, 2009

Students

  • Sangjun Moon (PhD Student, Stochastic optimization)
  • Heegun Kang (Parallel computing for linear programing)
  • Sanghee Chae (Topic model)
  • I Jeong Han
  • SungChul Hong
  • Yeon-Seon Cho
  • Eun-Young Ko

Alumni

  • Sungjun Byun (Seoul National University, Bundang Hospital)