Continuously additive models for nonlinear functional regression

70 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Partially Linear Functional Additive Models for Multivariate Functional Data (2018, unknown): “There is also another class of functional additive regression models proposed by Müller, Wu, and Yao (2013), McLean et al. (2014), and Kim et al. (2018), which offer an alternative view on modeling nonlinear effects of functional covariates.”
Functional Data Analysis (2016, methodology): “In the limit p → ∞ this becomes the continuously additive model (M¨uller, Wu & Yao 2013) (24) This model can be implemented with a bivariate spline representation of the function g ; a very similar model was introduced under the name functional generalized additive model in McLean et al. (2014).”