Asymptotic Properties of Principal Component Projections with Repeated Eigenvalues

Abstract

In FPCA methods, it is common to assume that the eigenvalues are distinct in order to facilitate theoretical proofs. We relax this assumption, provide a stochastic expansion for the estimated functional principal component projections, and establish their asymptotic normality.

Publication
In Statistics & Probability Letters
Justin Petrovich
Justin Petrovich
Associate Professor of Statistics and Business Data Analytics

My research interests include functional data analysis, longitudinal data analysis, and applied statistics.