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.
Type
Publication
In Statistics & Probability Letters
Justin Petrovich
Associate Professor of Statistics and Business Data Analytics
My research interests include functional data analysis, longitudinal data analysis, and applied statistics.