Generalized Functional Concurrent Model

• Activity Number: 560
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Isolated Statisticians
• Author(s): Janet Kim*+ and Arnab Maity and Ana-Maria Staicu
• Companies: North Carolina State University and North Carolina State University and North Carolina State University
• Keywords: Functional data analysis ; Generalized Functional Concurrent Model ; Penalized B-splines ; Tensor Product Smooth

Abstract

We consider the generalized functional model, where both the response and the covariate are functional data and are observed on the same domain. In contrast to typical functional linear concurrent models, we allow the relationship between the response and covariate to be nonlinear, depending on both the value of the covariate at a specific time point as well as the time point itself. In this framework we develop methodology for estimation of the unknown relationship and construction of point-wise confidence bands, allowing for correlated error structure as well as sparse and/or irregular design. We investigate this approach in finite sample size through simulations and a real data application.