Abstract
A recent issue in statistical analysis is modelling data when the effect variable
changes at different locations. This can be difficult to accomplish when the dimensions
of the covariates are very high, and when the domain of the varying coefficient
functions of predictors are not necessarily regular. This research paper will investigate
a method to overcome these challenges by approximating the varying coefficient
functions using bivariate splines. We do this by splitting the domain of the varying
coefficient functions into a number of triangles, and build the bivariate spline functions
based on this triangulation. This major paper will outline detailed theoretical results
of this method, and provide simulation studies to demonstrate the efficiency of this
approach. Finally, to illustrate the application of this method, we analyze heart
disease dataset where the given covariates are in spatially varying form.
Primary Advisor
Dr. Sévérien Nkurunziza
Program Reader
Dr. Mohamed Belalia
Degree Name
Master of Science
Department
Mathematics and Statistics
Document Type
Major Research Paper
Convocation Year
2023