Document Type
Poster
Publication Date
Summer 2024
Abstract
Functional Data Analysis (FDA) is a topic of growing interest in the Statistics community. The data in FDA are smooth curves or surfaces in time or space which can be conceptualized as functions.
We propose a Functional Generalized Linear Mixed Model (FGLMM) to fit EEG data and estimate the parameters using Quasi-Monte Carlo Method.
This proposed model deals with non-Gaussian scalar response, functional predictor, and random effects. We relax the assumption of link and variance functions.
Department
Statistics
WMU ScholarWorks Citation
Jayamaha, Ruvini, "Quasi – Monte Carlo Estimation for Functional Generalized Linear Mixed Models" (2024). Waldo Library Student Exhibits. 9.
https://scholarworks.wmich.edu/student_exhibits/9
Comments
Dr. Hyun Bin Kang, Faculty Advisor