讲座题目：Boosting methods for interval censored data with regression and classification

讲座时间：2025年9月21日（周一）10:50-11:40

讲座地点：犀浦校区3号教学楼X30425

讲座内容简介：

Boosting has gained significant interest across both machine learning and statistical communities. Traditional boosting algorithms, designed for fully observed random samples, often struggle with real-world problems, particularly with interval censored data. This type of data is common in survival analysis where exact event times are unobserved but fall within known intervals. Effective handling of such data is crucial in fields like medical research, reliability engineering, and social sciences. In this work, we introduce novel nonparametric boosting methods for regression and classification tasks with interval censored data. Our approaches leverage censoring unbiased transformations to adjust loss functions and impute transformed responses while maintaining model accuracy. Implemented via functional gradient descent, these methods ensure scalability and adaptability. We rigorously establish their theoretical properties. Our proposed methods not only offer a robust framework for enhancing predictive accuracy in domains where interval censored data are common but also complement existing work, expanding the applicability of existing boosting techniques. Empirical studies demonstrate robust performance across various finite-sample scenarios, highlighting the practical utility of our approaches.

主讲人简介：Wenqing He：1986年从兰州大学成人动漫专业获得学士学位，1989年从四川大学获得应用成人动漫硕士学位，1996年在加拿大约克大学获得统计学硕士学位，2002年从滑铁卢大学获得统计学哲学博士学位，2004年加入西安大略大学，现为西安大略大学统计与精算科学系教授、博士生导师。Wenqing He教授长期从事统计的理论和应用的研究工作，其研究领域涉及生存分析，高维数据分析，统计学习，统计计算等。其研究成果发表在国际著名期刊《The Journal of the Royal Statistical Society, Series B》《Bioinformatics》《Biometrics》《Statistica Sinica》《Technometrics》《Journal of Multivariate Analysis》《Statistics in Medicine》等。现为Statistics in Bioscience, Lifetime Data Analysis Journal of Statistical Distributions and Applications 等杂志副主编。他是泛华统计协会(ICSA)加拿大分会现任主席。