2015 QPRC

Generalized Function-on-Function Regression

Janet Kim, North Carolina State University 

Abstract: We consider a non-linear regression models for functional responses and functional predictors observed on possible different domains. We introduce a flexible model where the mean current response at a particular time point depends on the time point itself as well as the entire covariate trajectory. There are two innovations in this paper. First, we develop estimation methodology that accommodates realistic scenarios such as correlated error structure as well as sparse and/or irregular design, and prediction that accounts for unknown model correlation structure. Second we propose inference procedure that reduces the dimension of model parameters by orthogonal projection of the functional response. We investigate our methodology in finite sample size through simulations and real data application.