Recursive Partitioning Methods for Life History Processes

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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.