Restricted likelihood ratio tests for linearity in scalar-on-function regression
We propose a procedure for testing the linearity of a scalar-on-function regression relationship. To do so, we use the functional generalized additive model (FGAM), a recently developed extension of the functional linear model. For a functional covariate \(X(t)\), the FGAM models the mean response as the integral with respect to \(t\) of \(F\{X(t),t\}\) where \(F(\cdot ,\cdot )\) is an unknown bivariate function. The FGAM can be viewed as the natural functional extension of generalized additive models. We show how the functional linear model can be represented as a simple mixed model nested within the FGAM. Using this representation, we then consider restricted likelihood ratio tests for zero variance components in mixed models to test the null hypothesis that the functional linear model holds. The methods are general and can also be applied to testing for interactions in a multivariate additive model or for testing for no effect in the functional linear model. The performance of the proposed tests is assessed on simulated data and in an application to measuring diesel truck emissions, where strong evidence of nonlinearities in the relationship between the functional predictor and the response are found.
restricted likelihood ratio test generalized additive model functional data analysis functional regression p-spline p-spline analysis of variance