Functional Response Models for Addressing Outliers and Beyond

• Activity Number: 506
• Type: Invited
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Xin Tu* and Tian Chen and Wan Tang
• Companies: University of Rochester and University of Rochester and University of Rochester
• Keywords: between-subject attribute ; longitudinal data ; missing at random ; rank regression ; semi-parametric models

Abstract

Functional response models (FRM) are widely used to address limitations of classic statistical models as well as timely issues arising in research and practice. In this paper, we discusses two most recent applications of this class of models: one in robust regression analysis against outliers and the other on modeling human interaction. Popular semi-parametric regression models such as the generalized estimating equations (GEE) improve robustness of inference over parametric models. However, such models are not robust against outlying observations. The rank regression (RR) provides more robust estimates over the GEE. Unfortunately, the RR and its recent extensions to longitudinal data do not sufficiently address missing data. We discuss a new FRM-based approach to address outliers in longitudinal studies with missing data following the missing at random (MAR) mechanism. The second application focuses on modeling between-subject attributes, a new data type arising in modeling human interaction such as friendship between individuals. Such attributes are both conceptually and analytically differerent from whtin-subject attributes in conventional statistical models. The FRM is uniquely positioned to model this new data type.