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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709213
• 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。 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。第一个阶段是更好地理解生命历史过程，并研究这些过程中的复杂依赖结构。我将利用基于Copula的模型来制定依赖结构，并考虑对边际分析进行稳健推理，以减少边际模型错误指定对生活史过程联合分析的影响。第二阶段涉及对各种类型的粗化寿命数据的递归划分，包括右审查和区间审查。在第三阶段，基于前两个阶段开发的方法和算法，递归划分方法将扩展到生活史数据。 预计这项拟议的研究将对生命史过程的研究做出重大贡献，并使加拿大许多科学领域受益，这些领域处理生活史数据，需要识别风险群体并进行预测。该课程将为硕士和博士研究生在随机相关性建模、渐近方法、稳健推理和计算方法领域提供极好的培训机会。