Recursive Partitioning Methods for Life History Processes

生命史过程的递归划分方法

• 批准号: RGPIN-2016-04396
• 负责人: Diao, Liqun
• 金额: $ 1.31万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=649237
• 关键词: Recursive; Partitioning; Methods; Life; History

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.******Due to the challenging nature of research topic, the research objective will be realized gradually through the following three research stages. The first stage is to better understand life history processes and study complex dependence structure in such processes. I will utilize copula-based models to formulate dependence structure and consider robust inference for marginal analysis to reduce the effect of misspecification of marginal models to the joint analysis of life history processes. The second stage concerns recursive partitioning for various types of coarsened lifetime data including right-censoring and interval-censoring. In the third stage, the recursive partitioning methods will be extended to life history data based on methodologies and algorithms developed in the first two stages.******The proposed research is expected to significantly contribute to the study of life history processes and benefit many scientific fields in Canada which deal with life history data and have need to identify risk groups and make prediction. This program will provide excellent training opportunities for graduate students at both the master's and doctoral level in the fields of stochastic dependence modelling, asymptotic methods, robust inference and computational methods.**

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。本研究计划的目的是为生活史过程提供一个新的递归划分方法的综合说明。由于研究课题的挑战性，研究目标将通过以下三个研究阶段逐步实现。第一阶段是更好地理解生活史过程，研究这种过程中复杂的依赖结构。我将利用Copula为基础的模型，制定依赖结构，并考虑强大的边际分析的推理，以减少错误的边际模型的影响，生活史过程的联合分析。第二阶段涉及递归划分的各种类型的粗寿命数据，包括右删失和区间删失。在第三阶段，递归划分方法将扩展到基于前两个阶段开发的方法和算法的生活史数据。预计拟议的研究将大大有助于生活史过程的研究，并有利于加拿大处理生活史数据的许多科学领域，需要确定风险群体并进行预测。该计划将为硕士和博士生在随机相关建模，渐近方法，鲁棒推理和计算方法领域提供极好的培训机会。