SPEAKER:  Hans-Georg Mueller

ABSTRACT:

Functional regression has emerged as a useful approach for the analysis of complex data that combine functional or longitudinal predictors with scalar or functional responses. A major emphasis has been the functional linear regression model, which allows to implement dimension reduction in a simple and straightforward way but may be too restrictive. We will discuss flexible extensions of this model. These include functional quadratic, polynomial and
additive models. Of special interest is differentiation with respect to a functional argument, for which additive models are particularly well suited. Another extension are local models, where the focus is on the dependency of a Gaussian process or its derivatives at a given time on the value of a predictor process at the same or a different time. The methods will be illustrated with densely as well as sparsely sampled functional data. This talk is based on joint work with Wenjing Yang and Fang Yao.