| 主讲人简介： | Grace Y. Yi is a Professor and Tier I Canada Research Chair in Data Science at the University of Western Ontario. Her research interests focus on statistical methodology to address challenges concerning measurement error, causal inference, missing data, high-dimensional data, and statistical machine learning. She authored the monograph "Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application" (2017, Springer) and co-edited "Handbook of Measurement Error Models" (Grace Y. Yi, Aurore Delaigle, and Paul Gustafson, 2021, Chapman & Hall/CRC). Professor Yi is a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, and an Elected Member of the International Statistical Institute. In 2010, she received the Centre de Recherches Mathématiques and the Statistical Society of Canada (CRM-SSC) Prize. Professor Yi is a Co-Editor-in-Chief of The Electronic Journal of Statistics (2022-2024) and the Editor of the Statistical Methodology and Theory Section for The New England Journal of Statistics in Data Science. She was the Editor-in-Chief of The Canadian Journal of Statistics (2016-2018). She is currently the chair of the Lifetime Data Science Section of the American Statistical Association. She was the President of the Statistical Society of Canada (2021-2022) and the Founder of the first chapter (Canada Chapter, established in 2012) of the International Chinese Statistical |
| 讲座简介： | In survival data analysis, the Cox proportional hazards (PH) model is perhaps the most widely used model to feature the dependence of survival times on covariates. While many inference methods have been developed under such a model or its variants, those models are not adequate for handling data with complex structured covariates. High-dimensional survival data often entail several features: (1) many covariates are inactive in explaining the survival information, (2) active covariates are associated in a network structure, and (3) some covariates are error-contaminated. To handle such survival data, we propose graphical PH measurement error models and develop inferential procedures for the parameters of interest. Our proposed models significantly enlarge the scope of the usual Cox PH model and have great flexibility in characterizing survival data. Theoretical results are established to justify the proposed methods. Numerical studies are conducted to assess the performance of the proposed methods. |
