Robust Methods for the Generalized Functional Linear Model

• Activity Number: 383
• Type: Topic Contributed
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Melody Denhere*+
• Companies: University of Mary Washington
• Keywords: functional data ; outliers ; robust methods ; generalized functional linear model

Abstract

The last decade or so has seen a lot of interest emerging in the field of functional data analysis. This interest spans from a broad spectrum of fields such as brain imaging studies, bio-metrics, genetics, e-commerce and computer science. Statistical tools, models and methods, whose strength is in recognizing this structural aspect of data are being discussed and developed. In this work, we discuss robust estimation methods for the generalized functional linear model. The aim of these approaches is to minimize the effect of outlying curves in estimating the parameters of the model. Three estimation approaches are discussed; the first one makes use of robust principal component estimation techniques; the second one uses a robust penalization approach; and the last one uses rank estimation. Results from a simulation study and a real world example are also presented to illustrate the performance of the proposed estimators.