Functional Principal Component Analysis of Ordinal Functional Data

Music City Center 

Traditional functional principal component analysis (FPCA) is typically designed for continuous functional observations. In this paper, we address the scenario where the outcome consists of repeated categorical data defined over a bounded interval. Our objective is to develop an FPCA methodology tailored specifically for ordinal functional data. Our approach leverages recent advancements in ordinal data modeling to estimate both the mean function and the eigenfunctions, while employing a computationally efficient method for predicting the scores. The performance of the proposed methodology is evaluated numerically in simulation and data application.

Functional Data

Functional Principal Component Analysis

Ordinal Functional Data 

Section on Nonparametric Statistics