  In this paper, we propose a model averaging approach for addressing model
uncertainty in the context of partial linear functional additive models. These
models are designed to describe the relation between a response and mixed-types
of predictors by incorporating both the parametric effect of scalar variables
and the additive effect of a functional variable. The proposed model averaging
scheme assigns weights to candidate models based on the minimization of a
multi-fold cross-validation criterion. Furthermore, we establish the asymptotic
optimality of the resulting estimator in terms of achieving the lowest possible
square prediction error loss under model misspecification. Extensive simulation
studies and an application to a near infrared spectra dataset are presented to
support and illustrate our method.
