题目: Statistical Inference for Superposed Renewal Processes 叠加更新过程数据分析

演讲人: 叶志盛 新加坡国立大学

时间: 2020年12月31日上午10:00

地点: 线上

腾讯会议 ID：170 122 747 密码：123456

讲座摘要: Superposition of renewal processes is common in practice, and it is challenging to estimate the distribution of the individual inter-occurrence time associated with the renewal process. This is because with only aggregated event history, the link between the observed recurrence times and the respective renewal processes are completely missing, rendering inapplicability of existing theory and methods. In this talk, we propose a nonparametric procedure to estimate the inter-occurrence time distribution by properly deconvoluting the renewal equation with the empirical renewal function. Our theoretical analysis establishes the consistency and asymptotic normality of the nonparametric estimators. The proposed nonparametric distribution estimators are then utilized for developing theoretically valid and computationally efficient inferences when a parametric family is assumed for the individual renewal process. Compared with the existing maximum likelihood method, the proposed parametric estimation procedure is much faster, and the proposed estimators are more robust to round-off errors in the observed data.

演讲嘉宾简介: 叶志盛，博士，新加坡国立大学工业系统工程与管理系副教授。2008年获得清华大学材料科学与工程、经济学双学士学位，博士毕业于新加坡国立大学。叶教授的主要研究方向包括应用概率、统计相依模型、退化分析、可靠性建模以及随机管理等。在Technometrics，Journal of Quality Technology，Naval Research Logistics，IISE Transactions等国际知名期刊上发表高水平论文60余篇。