Functional interaction-based nonlinear models with application to multi-platform genomics data

Functional regression allows for a scalar response to be dependent on a functional predictor, however, not much work has been done when a scalar exposure that interacts with the functional covariate is introduced. In this paper, we present two functional regression models that account for this interaction and propose two novel estimation procedures for the parameters in these models. These estimation methods allow for a noisy and/or sparsely observed functional covariate and are easily extended to generalized exponential family responses. We compute standard errors of our estimators, which allows for further statistical inference and hypothesis testing. We compare the performance of the proposed estimators to each other and to one found in the literature via simulation and demonstrate our methods using a real data example.