报告题目：Estimating the Inter-Occurrence Time Distribution From Superposed Renewal Processes

报 告 人：叶志盛 新加坡国立大学 副教授

报告时间：2021年3月31日 下午 16:00-17:00

报告地点：腾讯会议

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会议 ID：514 302 527

校内联系人：赵世舜 zhaoss@jlu.edu.cn

报告摘要：

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. By carefully controlling the discretization errors and properly handling challenges due to implicit and non-smooth mapping via the renewal equation, 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. Comprehensive simulations show that 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.

报告人简介：

叶志盛副教授于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。现任新加坡国立大学工业系统工程与管理系副教授。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，IEEE Transactions on Reliability等国际知名期刊上发表高水平论文60余篇。