所属单位：数学与统计学院
教研室：信息与计算科学教研室
发表刊物：AIMs mathematics
项目来源：国家自然科学基金项目
关键字：survival tree; interval-censored; conditional inference framework; hyper-parameter tuning; generalized log-rank tests
摘要：Interval-censored failure time data as a general type of survival data often arises in medicine and other applied fields. Survival tree is a flexible predictive method for survival data because no specific assumptions are required. Generalized Log-Rank Test have good power with parameters for interval-censored failure time data. We construct a special test statistic of Generalized Log-Rank Tests, and propose a new survival tree with hyper-parameter by combining the test statistic with Conditional Inference Framework for interval-censored failure time data. The effect of tuning hyper-parameter are discussed and hyperparameter tuning allows the tree method to be more general and flexible. Thus the tree method either improve upon or remain competitive with existing tree method for interval-censored failure time data-ICtree, which is a special case of ours. An extensive simulation is executed to assess the predictive performance of our tree methods. Finally, the tree methods are applied to a tooth emergence data.
合写作者：Renato De Leone
第一作者：陈嘉
论文类型：期刊论文
卷号：10
期号：7
页面范围：1
字数：1
ISSN号：2473-6988
是否译文：否
发表时间：2022-07-10