Publications

*: Corresponding Author.

^: Students Supervised. 

Publications

1. J Shin, SJ Shin, S Bang (2025+) A least distance estimator for a multivariate regression model using deep neural networks, Journal of Statistical Computation and Simulation, accepted. 

2. SH Lee, JM Cha, SJ Shin (2024) Personalized prediction of survival rate with combination of penalized Cox models in patients with colorectal cancer, Medicine,  103 (24):p e38584.

3. JM Shin^, SJ Shin, S Bang (2024) Simultaneous estimation and variable selection for a non-crossing multiple quantile regression using deep neural networks, Statistics and Computing, Vol. 34, Article #102.

4. H Jang^, SJ Shin*, A Artemiou (2023) Principal Weighted Least Square Support Vector Machine: An Online Dimension-Reduction Tool for Binary Classification, Computational Statistics & Data Analysis, 187, 107818. 

5. M Park^, H Kim^, SJ Shin* (2023) L1-penalized Fraud Detection Support Vector Machines, Journal of the Korean Statistical Society, 52, p.420–439.

6. JK Kang and SJ Shin* (2022) A Forward Approach for Sufficient Dimension Reduction in Binary Classification, Journal of Machine Learning Research, 23, p.1-31.

7. Song M, Choi T,  SJ Shin, Jung Y, and Choi S (2022) Regularized Linear Censored Quantile Regression, Journal of the Korean Statistical Society, 51, p. 589–607.

8. H Kim^, I Sohn, SJ Shin* (2021) Regularization paths of L1-penalized ROC Curve-Optimizing Support Vector Machines, STAT, 10(1), e400.

9. A Artemiou, Y Dong, and SJ Shin (2021) Real-time sufficient dimension reduction through principal least squares support vector machines, Pattern Recognition, 112, 107768.

10.  H Kim^ and SJ Shin* (2021) A quantile-slicing approach for sufficient dimension reduction with censored responses, Biometrical Journal, 63(1), p.201-212.

11. SJ Shin, Y Wu, and H Ning (2020) A backward procedure for change-point detection with applications to copy number variation detection, The Canadian Journal of Statistics, 48(3), p.366-385

12. SJ Shin, J Li, J Ning, J Bojadzieva, L Strong and W Wang (2020) Bayesian estimation of a semiparametric recurrent event model with applications to the penetrance estimation of multiple primary cancers in Li-Fraumeni Syndrome, Biostatistics, 21 (3), p.467–482. 

13. SJ Shin (2020) Book Review: Model-Based Clustering and Classification for Data Science: With Applications in R, The American Statistician, 74 (2), p.208-209. 

14. D Kim^ and SJ Shin* (2020) The regularization paths for the ROC-optimizing support vector machines, Journal of the Korean Statistical Society,  49 (1), p.264–275

15. SJ Shin, EB Dodd, F Gao, J Bojadzieva, J Chen, X Kong, C Amos, J Ning, LC Strong, W Wang (2020) Penetrance estimates over time to first and second primary cancer diagnosis in families with Li-Fraumeni syndrome: a single institution perspective, Cancer Research,  80 (2), p.347-353.

16. SJ Shin, EB Dodd, G Peng, J Bojadzieva, J Chen, C Amos, PL Mai, SA Savage, ML Ballinger, DM Thomas, Y Yuan, LC Strong, W Wang (2020) Penetrance of different cancer types in families with Li-Fraumeni syndrome: a validation study using multi-center cohorts, Cancer Research,  80 (2), p.354-360.

17. SJ Shin, LC Strong, J Bojadzieva, W Wang, and Y Yuan (2019) Bayesian semiparametric estimation of cancer specific age-at-onset penetrance with application to Li-Fraumeni syndrome, Journal of the American Statistical Association, 114 (526), p.541-552.

18. BY Kim^, SJ Shin* (2019) Principal weighted logistic regression for sufficient dimension reduction in binary classification, Journal of the Korean Statistical Society, 48 (2), p. 194-206.

19. H Kim^, Y Wu, and SJ Shin* (2019) Quantile-slicing estimation for dimension reduction in regression, Journal of Statistical Planning and Inference, 198, p.1-12.

20. J Kang, SJ Shin, J Park, and S Bang (2018) Hierarchically penalized quantile regression with multiple responses, Journal of the Korean Statistical Society, 47 (4), p.471-481.

21. J Song^ and SJ Shin* (2018) Stability approach to selecting the number of principal components, Computational Statistics, 33(4), p.1923-1938.

22. C Wang, SJ Shin, and Y Wu (2018) Principal quantile regression for sufficient dimension reduction with heterogeneity, Electronic Journal of Statistics, 12(2), 2114-2140.

23. SJ Shin, C Zheng and Y Wu (2017) Invited discussion of "Random-projection ensemble classification" by TI Cannings and RJ Samworth, Journal of the Royal Statistical Society: Series B, 79(4), p.1021-1022.

24. KH Kim and SJ Shin* (2017) The cumulative Kolmogorov filter for model-free screening in ultrahigh dimensional data, Statistics and Probability Letters, Vol. 126, p.238–243.

25. SJ Shin* and A Artemiou (2017) Penalized principal logistic regression for sparse sufficient dimension reduction, Computational Statistics and Data Analysis, Vol. 111, p.48-58.

26. SJ Shin, Y Wu, HH Zhang, and Y Liu (2017) Principal weighted support vector machines for sufficient dimension reduction in binary classification, Biometrika, 104(1), p.67-81.

27. SJ Shin* and SK Ghosh (2017) A comparative study of the dose-response analysis with application to the target dose estimation, Journal of Statistical Theory and Practice, 11(1), p.145-162.

28. SJ Shin*, HH Zhang, and Y Wu (2017) A nonparametric survival function estimator via censored kernel quantile regressions, Statistica Sinica, 27(1), p.457-478.

29. SJ Shin, Y Wu, HH Zhang, and Y Liu (2014) Probability-enhanced sufficient dimension reduction for binary classification, Biometrics, 70(3), p.546-555. 

30. SJ Shin and Y Wu (2014) Discussion: variable selection in large margin classifier-based probability estimation with high-dimensional predictors, Biometrical Journal, 56(4), p.594-596.

31. SJ Shin, Y Wu and, HH Zhang. (2014) Two-dimensional solution surface for weighted support vector machines, Journal of Computational and Graphical Statistics, 23(2), p.383-402.

32. M Jhun and SJ Shin (2009) Bootstrapping spatial median for location problems, Communications in Statistics: Simulation and Computation, 38(10), p.2123-2133.

Book Chapter

1. SJ Shin and Y Wu (2022) Penalized Regression in Computational Statistics in Data Science Edited by Walter W. Piegorsch, Richard A. Levine, Hao Helen Zhang, and Thomas C. M. Lee. Wiley.

PCAWG Evolution and Heterogeneity Working Group (Consortia Authorship)

1. Salcedo A, Tarabichi M, Buchanan A, et al. (2024) Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction. Nature Biotechnology. In press.

2. Dentro, SC., Leshchiner, I., Haase K. et al. (2021) Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes, Cell, 184 (8), p. 2239-2254

3. Campbell, P.J., Getz, G., Korbel, J.O. et al. (2020) Pan-cancer analysis of whole genomes, Nature. 578, p.82–93. [Media]

4. Gerstung, M., Jolly, C., Leshchiner, I. et al. (2020) The evolutionary history of 2,658 cancers. Nature, 578, p.122–128.

5. Cmero, M., Yuan, K., Ong, C.S. et al. (2020) Inferring structural variant cancer cell fraction, Nature Communications, 11, 730, doi.org/10.1038/s41467-020-14351-8.

6. Rubanova, Y., Shi, R., Harrigan, C.F. et al. (2020) Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig. Nature Communication 11, 731.

7. M Tarabichi et al. (2018) Neutral tumor evolution?, Nature Genetics, 50(12), p.1630-1633. 

Publications (in Korean Journals)

1. 김선우, 신승준 (2025) 고속도로 교통량 결정요인 탐색 및 딥러닝 모형을 통한 예측, 한국자료분석학회지, 27 (2). 

2. 박현우, 신승준, 윤견수 (2024) Change Point Detection을 이용한 대한민국 기온 변화 분석, 한국자료분석학회지, 26 (6), p.1777-1787

3. J Shin^ and SJ Shin* (2024) A Concise Overview of Principal Support Vector Machines and Its Generalization, Communications for Statistical Applications and Methods, 31, p. 235-246.

4. H Kim and SJ Shin (2024) L1-penalized AUC-optimization with a surrogate loss, Communications for Statistical Applications and Methods, 31, p.203-212 

5. 신승준, 서범석 (2024) 빅데이터를 이용한 실시간 소비예측, 응용통계연구, 37(1) p. 13–38. (Early Version in BOK 경제연구)

6. Lee S^, Yang M^, Kang J, and Shin SJ* (2022) Ensemble Variable Selection Using Genetic Algorithm, Communications for Statistical Applications and Methods, 29(6) p.629-640.

7. 박민형^, 신승준* (2022) 불량 웨이퍼 탐지를 위한 함수형 부정탐지 지지벡터기계, 용통계연구, 35(5) p. 1-9.

8. Yang S^, Shin SJ*, Sung W, and Lee C (2022) Naive Bayes Classifiers Boosted By Sufficient Dimension Reduction: Applications to Top-k Classification, Communications for Statistical Applications and Methods, 29(5) p.603-614.

9. Park S^ and Shin SJ* (2022) ADMM for Least Square Problems with Pairwise-Difference Penalties for Coefficient Grouping, Communications for Statistical Applications and Methods, 29(4) p.441-451.

10. Bae H^, Kim H, and Shin SJ* (2022) The use of Support Vector Machines in Semi-supervised classification, Communications for Statistical Applications and Methods, 29(1), p.193-202.

11. 신정민^, 김형우^, 신승준 (2021) 중도절단 회귀모형에서 역절단확률가중 방법 간의 비교연구, 응용통계연구, 34 (6), p.957-968.

12. Kang J, Yi J, Song H, Shin SJ*, and Kim J (2020) Association between in vitro fertilization success rate and ambient air pollution: a possible explanation of within-year variation of in vitro fertilization success rate. Obstetrics & Gynecology Science, 63(1), p.72-79.

13. Lee E^ and Shin SJ* (2019) A soft classification with a functional predictor, Communications for Statistical Applications and Methods. 26 (6), p.635-644.

14. J Song^ and SJ Shin* (2019) A Two-step approach for variable selection in linear regression with measurement error, Communications for Statistical Applications and Methods, 26 (1), p.47-55.

15. 김경희, 신승준 (2017) Adaptive ridge procedure for L0-penalized weighted support vector machines, 한국데이터정보과학회지, 28(6), p.1271-1278.

16. 이경은^, 김경희, 신승준 (2017) 초고차원 다범주분류를 위한 변수선별 비교연구, 응용통계연구, 30(5), p.793-808.

17. 방성완, 신승준 (2016) 비교차 제약식을 이용한 다중 선형 분위수 회귀모형에 관한 비교연구, 응용통계연구, 29(5), p.773-786.

18. 전명식, 신승준 (2007) 결측치 대체방법들에 대한 비교연구: 정준판별분석을 중심으로. 한국자료분석학회지, 9(2), p.673-685.