A goodness-of-fit test for the functional linear model with functional response
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The Functional Linear Model with Functional Response (FLMFR) is one of the most fundamental models to asses the relation between two functional random variables. In this paper, we propose a novel goodness-of-fit test for the FLMFR against a general, unspecified, alternative. The test statistic is formulated in terms of a Cramér-von Mises norm over a doubly-projected empirical process which, using geometrical arguments, yields an easy-to-compute weighted quadratic norm. A resampling procedure calibrates the test through a wild bootstrap on the residuals and the use convenient computational procedures. As a sideways contribution, and since the statistic requires from a reliable estimator of the FLMFR, we discuss and compare several regularized estimators, providing a new one specifically convenient for our test. The finite sample behavior of the test, regarding power and size, is illustrated via a complete simulation study. Also, the new proposal is compared with previous significance tests. Two novel real datasets illustrate the application of the new test.
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