A centroid classifier for functional/longitudinal data. Issue 5 (24th March 2022) - Darabi, Nadiyeh; Hosseini-Nasab, S. Mohammad E.
Abstract : The problem of classifying functional data has raised great research interests. When functional data are fully observed random trajectories, many classifiers have been recently proposed. However in practice, functional data may be observed discretely based on a regular or irregular design with the measurements that are contaminated by noise. In this paper, we define a new classifier to classify functional/longitudinal data based on the original discrete observations and under a general weighing scheme. We then study the theoretical properties of the proposed centroid classifier, and establish that the proposed classification rule is consistently estimated by its empirical version. The empirical performance is illustrated through a simulation study and two real data sets.