报告题目：Phase-Type Distributions for Survival Data with Two-Layer Censoring

报告人：叶志盛 副教授 新加坡国立大学

报告时间：2025年9月1日 11:00-12:00

报告地点：腾讯会议ID：865-627-787

校内联系人：赵世舜 [email protected]

报告摘要：

Survival data, such as warranty claims and disease registry data, are usually subject to two layers of right censoring. The first layer, which is called lifetime censoring, applies to the lifetime due to a fixed warranty limit or a competing risk. The second layer, which is called end-of-study censoring, applies to the sum of the sales lag (or reporting delay) and the lifetime due to the end-of-study date for the data collection. An unreported subject would either have a lifetime longer than the lifetime censoring limit or the sum of the sales lag (or reporting delay) and the lifetime longer than the end-of-study date. The two-layer censoring in the lifetime data renders traditional nonparametric methods for right-censored data inapplicable. This study develops a generic method for the two-layer censored data using acyclic phase-type distributions (APHDs) in the canonical form. The APHD estimators can be regarded as nonparametric sieve estimators since the family of APHDs is dense in the field of all positive-valued distributions. Based on the property that the class of APHDs is closed under convolution, a dedicated expectation-maximization algorithm is proposed for parameter estimation. Comprehensive simulations are conducted to evaluate the performance and compare with the inverse probability of censoring weighted approach, which is applicable in the absence of lifetime censoring. Two real examples are used to illustrate the proposed method.

报告人简介：

叶志盛博士本科毕业于清华大学材料科学与工程系，博士就读于新加坡国大工业与系统工程系。现在为新加坡国大工业系统工程与管理系副教授。他的主要研究方向包括剩余寿命预测，可靠性建模，及数据驱动的运营决策。