Functional regression with repeated eigenvalues

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.