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.
Original language | English (US) |
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Pages (from-to) | 42-48 |
Number of pages | 7 |
Journal | Statistics and Probability Letters |
Volume | 130 |
DOIs | |
State | Published - Nov 2017 |
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty