Conditional Modeling of Panel Count Data with Partly Interval-censored Failure Event

发布时间：2024-06-18 作者：浏览次数：

Speaker:	胡翔斌	DateTime:	2024年6月25日（周二）下午4:00-5:00
Brief Introduction to Speaker: 胡翔斌，香港理工大学应用数学系博士后。2017年6月获华中师范大学硕士学位，2021年3月获香港理工大学博士学位，随后先后于新加坡国立大学、香港理工大学从事博士后研究工作。主要研究方向为生存分析，经验过程理论，非参数与半参数模型分析，生成对抗网络。其研究成果在Biometrics, Bernoulli, Statistic in Medicine, Statistica Sinica等知名统计期刊上发表。		Place:	6号楼二楼报告厅
Abstract:In longitudinal follow-up studies, panel count data arise from discrete observations on recurrent events. We investigate a more general situation where a partly interval-censored failure event is informative to recurrent events. The existing methods for the informative failure event are based on the latent variable model, which provides indirect interpretation for the effect of failure event. To solve this problem, we propose a failure time-dependent proportional mean model with panel count data through an unspecified link function. For estimation of model parameters, we consider a conditional expectation of least squares function to overcome the challenges from partly interval-censoring and develop a two-stage estimation procedure by treating the distribution function of the failure time as a functional nuisance parameter and using the B-spline functions to approximate unknown baseline mean and link functions. Furthermore, we derive the overall convergence rate of the proposed esti...

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