Recursive Partitioning Methods for Life History Processes

生命史过程分析是统计科学的一个重要方面，在精算学、经济学、工程学、环境科学、管理学、医学、运筹学、公共卫生以及社会和行为科学等广泛领域都有应用。这一领域的许多科学问题都集中在各种可能粗略的事件时间和一组协变量之间的关系上，以便解释和预测。这种关系通常以倒退为特征。比例风险回归是终生数据最常用的参数(或半参数)回归。参数回归的一个主要问题是，如果模型假设不满足，统计推断可能会产生误导。递归划分方法是使用机器学习技术的强大的非参数选择。它们很有吸引力，因为它们不需要指定模型结构，而且它们通常会产生具有直观解释的实际友好的模型，因此它们具有很大的潜力，很容易被从业者接受。现有的大多数递归划分文献仅限于对完全观察到的响应(分类的或连续的)或右删失的生存数据的分析，然而，具有多种类型数据粗化的复杂生活史数据仍有待开发。这项研究计划的目的是为生活史过程提供一种新的递归划分方法的综合描述。

The analysis of life history processes is an important aspect of statistical science with applications in a wide range of fields including actuarial science, economics, engineering, environmental sciences, management, medicine, operations, public health, and social and behavioural sciences. Many scientific problems in the area focalize interests in the relationship between various possibly coarsened event times and a set of covariates for the purposes of explanation and prediction. Such relationship is conventionally characterized by regression. Proportional hazard regression is the most commonly-used parametric (or semiparametric) regression for lifetime data. One major concern for parametric regression is that statistical inference can be misleading if model assumptions are not satisfied. Recursive partitioning methods are powerful non-parametric alternatives using machine-learning techniques. They are appealing since they require no specification of the model structure and they usually lead to practically friendly models with intuitive interpretation so that they have great potential to be easily accepted by practitioners. Most existing literature of recursive partitioning is restricted to the analysis of completely observed responses (categorical or continuous) or right-censored survival data, however, complex life history data with multiple types of data coarsening remain to be developed. The objective of this research proposal is to provide a comprehensive account of novel recursive partitioning methods for life history processes.