Asymptotic Properties of Principal Component Projections with Repeated Eigenvalues


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.

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

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