Date of Award
5-2019
Document Type
Thesis
Degree Name
Master of Science (MS)
College/School
College of Science and Mathematics
Department/Program
Mathematical Sciences
Thesis Sponsor/Dissertation Chair/Project Chair
Andrada E. Ivanescu
Committee Member
Andrew McDougall
Committee Member
Haiyan Su
Abstract
We study the data setting consisting of functional data sets repeatedly observed over time. The focus is on the dynamic prediction of the future trajectory for a subject. Regression methods based on dynamic functional models are used for dynamic prediction of individual trajectories. We propose strategies for the selection of the study sampling design in the context of longitudinal functional data. An application to simulated child growth data is presented. The height-for-age z-score (HAZ) was the response variable in the functional dynamic models for prediction. The intent was to recommend four months for removal in our initial historic data set. We quantify the effect on dynamic prediction performance when several data missing scenarios and methods of data imputation were considered. The effectiveness of seven methods of data imputation in the setting of longitudinal functional data were examined.
File Format
Recommended Citation
Jassel, Toni, "Sampling Studies for Longitudinal Functional Data" (2019). Theses, Dissertations and Culminating Projects. 269.
https://digitalcommons.montclair.edu/etd/269
Included in
Applied Statistics Commons, Longitudinal Data Analysis and Time Series Commons, Mathematics Commons