报告题目：Local composite partial likelihood estimation for length-biased and right-censored data
报 告 人：徐达博士 上海财经大学
报告时间：2019年10月16日上午11:00-12:00
报告地点：数学楼629室
报告摘要：
Length-biased data, which are often encountered in engineering, economics and epidemiology studies, are generally subject to right censoring caused by the research ending or the follow-up loss. The structure of length-biased data is distinct from conventional survival data, since the independent censoring assumption is often violated due to the biased sampling. In this paper, a proportional hazard model with varying coefficients is considered for the length-biased and right-censored data. A local composite likelihood procedure is put forward for the estimation of unknown coefficient functions in the model, and large sample properties of the proposed estimators are also obtained. Additionally, an extensive simulation studies are conducted to assess the finite sample performance of the proposed method and a data set from the Academy Awards is analyzed.
报告人简介：
徐达，上海财经大学博士后，主要从事纵向数据和生存数据分析等方面的研究，目前在Lifetime Data Analysis、Journal of Applied Statistics、Journal of Nonparametric Statistics等期刊上发表高水平科研论文多篇。
