| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 5129643 | Statistics & Probability Letters | 2017 | 7 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Justin Petrovich, Matthew Reimherr,