Recursive Partitioning Methods for Life History Processes

生命史过程的分析是统计科学的一个重要方面，其应用领域广泛，包括精算学、经济学、工程学、环境科学、管理学、医学、运营学、公共卫生以及社会和行为科学。该领域的许多科学问题集中在各种可能的粗化事件时间和一组协变量之间的关系，以解释和预测为目的。这种关系通常以回归为特征。比例风险回归是寿命数据最常用的参数（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。递归分割方法是使用机器学习技术的强大的非参数替代方法。他们是有吸引力的，因为他们不需要规范的模型结构，他们通常会导致实际友好的模型与直观的解释，使他们有很大的潜力，很容易被从业者接受。现有的大多数递归划分的文献仅限于分析完全观察到的反应（分类或连续）或右删失生存数据，然而，复杂的生活史数据与多种类型的数据粗化仍有待开发。这项研究的目的是提供一个全面的考虑新的递归划分方法的生活史过程。

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.