B1018
Title: Non-linear function-on-function regression via neural nets
Authors: Matthew Reimherr - Pennsylvania State University (United States) [presenting]
Aniruddha Rao - Pennsylvania State University (United States)
Abstract: A new class of non-linear function-on-function regression models for functional data using neural networks is introduced. We propose a framework using a hidden layer consisting of continuous neurons, called a continuous hidden layer, for functional response modeling and give two model-fitting strategies, Functional Direct Neural Networks (FDNN) and Functional Basis Neural Networks (FBNN). Both are designed explicitly to exploit the structure inherent in functional data and capture the complex relations existing between the functional predictors and the functional response. We fit these models by deriving functional gradients and implementing regularization techniques for more parsimonious results. We demonstrate the power and flexibility of our proposed method in handling complex functional models through extensive simulation studies as well as real data examples.
