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

Academic Position:

• 2018- : Associate Professor, Sun Yat-sen University, China

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

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

Education:

• 2010-2014: Ph.D. in Statistics, University of Waterloo, Canada 

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

• 2004-2008: B.S. in Mathematics, University of Science and Technology of China, China

Research Interest:

• Biostatistics, Multivariate Covariate Balancing, Measurement Error in Causal Inference, Mediation Analysis,  Policy Learning, Functional and Imaging Data Analysis, Data Integration

Selected Publication (* Student):

• Y. Dai* and Y. Yan (2024). Mahalanobis balancing: a multivariate perspective on approximate covariate balancing, Scandinavian Journal of Statistics, Published online.

• Y. Yan (2024).   Balancing covariate distributions using maximum mean discrepancy: a multivariate distance-based framework.

• Y. Yan and M. Ren* (2023). Consistent inverse probability of treatment weighted estimation of the average treatment effect with mismeasured time‐dependent confounders, Statistics in Medicine, 42, 517-535.

• Y. Yan, M. Ren*, and A. de Leon (2023). Measurement error correction in mediation analysis under the additive hazards model, Communications in Statistics-Simulation and Computation, Published online.

• X. Li*, Q. Zhou*, Y. Wu, and Y. Yan (2023). Multicategory matched learning for estimating optimal individualized treatment rules in observational studies with application to a hepatocellular carcinoma study, arXiv preprint arXiv:2302.05287 

• Y. Yan and L. Shen* (2023). Mediation analysis with exposure-mediator interaction and covariate measurement error under the additive hazards model.

• Y. Tan*, X. Wen*, W. Liang*, and Y. Yan (2022). Empirical likelihood-based weighted estimation of average treatment effects in randomized clinical trials with missing outcomes, Statistics and Its Interface, Accepted. 

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

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

• X. Wen*, Y. Yan, W. Pan, and X. Zhang (2021). Kernel-distance-based covariate balancing, arXiv preprint arXiv:2101.03463

• G. Y. Yi, Y. Yan, X. Liao, and D. Spiegelman (2019). Parametric regression analysis with covariate misclassification in main study/validation study designs, International Journal of Biostatistics, 15, 1-24. 

• N.Gugala, J. Lemire, K. Chatfield-Reed, Y. Yan, G. Chua, and R. J. Turner (2018). Using a chemical genetic screen to enhance our understanding of the antibacterial properties of silver, Genes, 9, 344.

• Y. Yan, H. Zhou, and J. Cai (2017). Improving efficiency of parameter estimation in case‐cohort studies with multivariate failure time data, Biometrics, 73, 1042-1052.

• Y. Yan and G. 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, 684-695. 

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

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

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

Ongoing Work:

• X. Wen* and Y. Yan (2023+). Covariate balancing with measurement error.

• X. Li* and Y. Yan (2023+). Matching-based policy learning.

• Y. Yan, H. Zhou, and J. Cai (2023+). Optimal parameter estimation in case-cohort studies with multivariate survival data.

• X. Li*, Q. Zhou*, W. Pan, Y. Yan, and C. Huang (2023+). Functional partially linear single-index Cox regression model.

• Z. Yao*, B. Yang*, Y. Yan, and Y. Wu. Covariate balancing for estimating restricted mean survival time in multi-arm observational studies. In progress.

• Z. Chen*, P. Han, and Y. Yan. Data integration with missingness. In progress.

Award:

• The GREAT Supervisor Award, University of Calgary, 2017.

• Pierre Robillard Award, Statistical Society of Canada, 2015.

Research Grant:

• Principal Investigator (PI), 2016-2020, Discovery Grant, Natural Sciences and Engineering Research Council of Canada (NSERC).

  • Project Title: Statistical Methods on Complex Survival Data.

• Co-PI, 2016-2017, Canadian Kidney Cancer Information System (CKCis).

  • Project Title: New Statistical Methodologies to Support CKCis.

• Co-PI, 2016-2017, Breast Cancer Society of Canada (BCSC).

  • Project Title: New Biostatistical Methodologies for the Analyses of Breast Cancer Data with Gene Expression.

• PI, 2015-2017, Start-up Grant, University of Calgary.

• PI/Co-PI,  2020-, NSFC grants.

Reviewer:

• Biometrics, Biostatistics, BMJ Open, Canadian Journal of Statistics,  Computational Statistics & Data Analysis, Journal of Applied Statistics, Journal of the American Statistical Association, Journal of the Royal Statistical Society: Series C,  Journal of Statistical Computation and Simulation, Lifetime Data Analysis,  Metrika, Statistics and Its Interface, Statistics in Medicine, Statistical Methods in Medical Research, Statistica Sinica, etc.