Ying Yan, School of Mathematics, Sun Yat-sen University

Academic Position:

• 2017-  : Associate Professor, Sun Yat-sen University (中山大学), China

• 2015-2017: Assistant Professor, University of Calgary, Canada

• 2014-2015: Postdoctoral Researcher, University of North Carolina at Chapel Hill, United States

Education:

• 2010-2014: Ph.D. in Statistics, University of Waterloo, Canada, Advisor: Dr. Grace Y. Yi 

• 2008-2010: M.Math. in Statistics, University of Waterloo, Canada

• 2004-2008: B.S. in Mathematics, University of Science and Technology of China (中国科学技术大学), China

Research Interest:

• biostatistics, causal inference, machine learning, measurement error, missing data, non-Euclidean data analysis, nonparametric and semiparametric statistics

Selected Publication/Manuscript († Supervised Student):

• Zhenhao Chen†, Peisong Han, and Ying Yan (2026). A semiparametric integration framework for modeling missing data with external summary information.

• Xuqiao Li†, Qiuyan Zhou†, Shengxian Ding, Wenliang Pan, Rongjie Liu, Ying Yan, and Chao Huang (2026). Shape-based partially linear single-index Cox model for Alzheimer's disease convention, Statistics in Medicine, revised.

• Xialing Wen† and Ying Yan (2026). Inference of treatment effects using restricted mean survival times in the presence of covariate measurement error: A corrected entropy balancing approach, Biometrical Journal, under revision.

• Xialing Wen†, Liangchen Qin†, Hui Wu†, and Ying Yan (2025). Survival analysis under the Aalen's additive hazards model with covariate measurement error: Application to causal mediation analysis, Statistics in Medicine, 44(28-30), e70346.

• Xialing Wen† and Ying Yan (2025). Covariate balancing with measurement error, Statistics in Medicine, 44(23-24), e70300.

• Ying Yan and Lingzhu Shen† (2025). Mediation analysis with exposure-mediator interaction and covariate measurement error under the additive hazards model, Biometrical Journal, 67(1), e70035.

• Xuqiao Li†, Qiuyan Zhou†, Ying Wu, and Ying Yan (2025). Multicategory matched learning for estimating optimal individualized treatment rules in observational studies with application to a hepatocellular carcinoma study, Statistical Methods in Medical Research, 34(3), 508-522.

• Ying Yan (2025). Orthogonal covariate balancing.

• Xuqiao Li† and Ying Yan (2024). Matching-based policy learning, arXiv:2407.08468. 

• Yimin Dai† and Ying Yan (2024). Mahalanobis balancing: A multivariate perspective on approximate covariate balancing, Scandinavian Journal of Statistics, 51(4), 1450-1471. 

• Yuanyao Tan†, Xialing Wen†, Wei Liang†, and Ying Yan (2024). Empirical likelihood-based weighted estimation of average treatment effects in randomized clinical trials with missing outcomes, Statistics and Its Interface, 17(4), 699-714. 

• Ying Yan, Mingchen Ren†, and Alexander de Leon (2024). Measurement error correction in mediation analysis under the additive hazards model, Communications in Statistics-Simulation and Computation, 53(10), 5083-5099.

• Ying Yan and Mingchen Ren† (2023). Consistent inverse probability of treatment weighted estimation of the average treatment effect with mismeasured time-dependent confounders, Statistics in Medicine, 42(4), 517-535.

• Wei Liang† and Ying Yan (2022). Empirical likelihood-based estimation and inference in randomized controlled trials with high-dimensional covariates, Statistics and Its Interface, 15(3), 283-301.

• Grace Y. Yi and Ying Yan (2021). Estimation and hypothesis testing with error-contaminated survival data under possibly misspecified measurement error models, Canadian Journal of Statistics, 49(3), 853-874.

• Grace Y. Yi, Ying Yan, Xiaomei Liao, and Donna Spiegelman (2019). Parametric regression analysis with covariate misclassification in main study/validation study designs, International Journal of Biostatistics, 15(1), 20170002. 

• Natalie Gugala, Joe Lemire, Kate Chatfield-Reed, Ying Yan, Gordon Chua, and Raymond J. Turner (2018). Using a chemical genetic screen to enhance our understanding of the antibacterial properties of silver, Genes, 9(7), 344.

• Ying Yan, Haibo Zhou, and Jianwen Cai (2017). Improving efficiency of parameter estimation in case-cohort studies with multivariate failure time data, Biometrics, 73(3), 1042-1052.

• Ying Yan and Grace Y. Yi (2016). A class of functional methods for error-contaminated survival data under additive hazards models with replicate measurements, Journal of the American Statistical Association, 111(514), 684-695. 

• Ying Yan and Grace Y. Yi (2016). Analysis of error-prone survival data under additive hazards models: measurement error effects and adjustments, Lifetime Data Analysis, 22(3), 321-342. 

• Ying Yan and Grace Y. Yi (2015). A corrected profile likelihood method for survival data with covariate measurement error under the Cox model,  Canadian Journal of Statistics, 43(3), 454-480. 

• Changbao Wu and Ying Yan (2012). Empirical likelihood inference for two-sample problems, Statistics and Its Interface, 5(3), 345-354.

Reviewer:

• Biometrical Journal, Biometrics, Biostatistics, Canadian Journal of Statistics, Computational Statistics & Data Analysis, Electronic Journal of Statistics, Journal of the American Statistical Association, Journal of Biopharmaceutical Statistics, Journal of Multivariate Analysis, Journal of the Royal Statistical Society: Series B, Journal of the Royal Statistical Society: Series C, Lifetime Data Analysis, Scandinavian Journal of Statistics, Statistics in Medicine, Statistical Methods in Medical Research, Statistica Sinica, Technometrics, etc.