Date of Graduation
12-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Engineering (PhD)
Degree Level
Graduate
Department
Industrial Engineering
Advisor/Mentor
Chimka, Justin R.
Committee Member
Nachtmann, Heather L.
Second Committee Member
Liu, Xiao
Third Committee Member
Hernandez, Sarah V.
Keywords
tree-based framework; interpretability; interaction effects; ensemble learning; overfitting; regression methods
Abstract
This dissertation introduces a tree-based framework to improve the interpretability and modeling of interaction effects among variables, essential in fields like biostatistics, healthcare, science and engineering. Traditional regression methods often fail to clearly capture complex interactions, while tree-based approaches, despite their interpretability, face performance limitations and overfitting concerns. Our proposed interaction-sensitive tree-based method, designed for seamless integration, combines various statistical techniques tailored to different data types, leveraging ensemble learning methods to enhance accuracy and mitigate overfitting. We present methods for regression, survival analysis, and classification, validated with case studies and benchmarked against traditional models using metrics like BIC and R-squared. The results highlight our framework’s balance between interpretability and predictive performance, offering a robust solution for analyzing complex data interactions.
Citation
Sun, X. (2024). Interaction-Sensitive Tree-Based Statistical Models. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/5579
