| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1151350 | Statistics & Probability Letters | 2015 | 9 Pages |
Abstract
We explore the functional principal component method for estimating regression parameters in functional linear models. We demonstrate that the commonly made assumption concerning unique eigenvalues is unnecessary. Convergence rates are established allowing a variety of sample spaces and dependence structures.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Matthew Reimherr,