PhD Public Lecture: Da Zhong (Dexen) Xi (DSAS)
Title: Statistical methods with a focus on joint outcome modeling and on methods for fire science
Abstract: Understanding the dynamics of wildland fires contributes significantly to the development of fire science. Challenges in the analysis of historical fire data include defining fire dynamics within existing statistical frameworks, modeling the duration and size of fires as joint outcomes, identifying the how fires are grouped into clusters of subpopulations, and assessing the effect of environmental variables in different modeling frameworks. We develop novel statistical methods to consider outcomes related to fire science jointly. These methods address these challenges by linking univariate models for separate outcomes through shared random effects, an approach referred to as joint modeling. Comparisons with existing approaches demonstrate the flexibilities of the joint models developed and the advantages of their interpretations. Models used to quantify fire behaviour may also be useful in other applications, and here we consider modeling disease spread. The methodologies for fire modeling can be used, for example, for understanding the progression of Covid-19 in Ontario, Canada.
The key contributions presented in this thesis are the following: 1) Developing frameworks for modelling fire duration and fire size in British Columbia, Canada, jointly, both through modelling using shared random effects and also through copulas. 2) Illustrating the robustness of joint models when the true models are copulas. 3) Extending the framework into a finite joint mixture to classify fires into components and to identify the subpopulation to which the fires belong. 4) Incorporating the longitudinal environmental variables into the models. 5) Extending the method into the analysis of public health data by linking the daily number of Covid-19 hospitalizations and deaths as time series processes using a shared random effect. A key aspect of the research presented here is the focus on extensions of the joint modeling framework.
