报告题目：Statistical inference for two Burr-XII populations under balanced joint progressive censoring with competing risks

报告摘要：In this talk, we propose a statistical inference framework for analyzing competing risks data under balanced joint progressive censoring using the Burr-XII distribution. This framework provides a systematic approach for comparative lifetime studies. We develop maximum likelihood estimation procedures for the unknown parameters and rigorously establish the existence and uniqueness of the resulting estimators. We also construct four types of confidence intervals: approximate confidence intervals (ACIs), lognormal ACIs, as well as bootstrap-p and bootstrap-t confidence intervals. For Bayesian inference, we present parameter estimation under three distinct loss functions and derive highest posterior density credible intervals through Markov Chain Monte Carlo (MCMC) simulations. The performance of all methods is comprehensively evaluated through simulation studies and real data analysis.

报告人简介：桂文豪，教授，博士生导师，现任北京交通大学数学与统计学院数据科学系主任。2009年获美国佛罗里达州立大学统计学博士学位。曾在美国康奈尔大学和明尼苏达大学任职。担任国家级一流课程负责人，主编教材5部。发表统计学论文130余篇，其中SCI检索论文110余篇。兼任中国科协海智计划特聘专家、教育部学位中心评议专家、教育部人事司评审专家、教育部考试研究院命题专家等职务。

报告时间：2026年4月15日（周三） 20:00-21:00

报告地点：武之楼412会议室