Principal Component Analysis for sparse functional data with application to handwriting data

Abstract

We consider principal component analysis for multi-dimensional sparse functional data. The mixed effect model and reduced rank model have been used for analyzing the sparse functional data. In this paper, we introduce a principal component method for the multi-dimensional sparse functional data based on the reduced rank model, and model selections will be performed by using Akaike information criterion (AIC) and Bayesian information criterion (BIC). Further more,the use of the proposed method is illustrated through the analysis of human gait data and handwriting data.