Identification of Outliers for Periodic Multivariate Functional Data

• Activity Number: 378
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 11:35 AM to 12:20 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Pallavi Sawant*
• Companies: Kansas State University
• Keywords: Stochastic process ; Multivariate functional data ; Functional outlier ; Dimension reduction

Abstract

In many real-life situations, we observe continuous functions known as univariate functional data. In this paper we consider multivariate functional data an extension of univariate case, which is rarely considered in literature. Each observation is a p dimensional stochastic process in multivariate functional data. In this paper the problems of analyzing periodic multivariate functional data in presence of functional outliers are discussed. The aim of this analysis is to identify functional outliers and deal with the analysis problem in the infinite dimensional setting and develop a new methodology. The approach used here is to project data onto a finite dimensional basis by using orthonormal property of Fourier series and then perform a robust multivariate analysis. The numerical results on simulated and real world dataset verify the effectiveness of the proposed method.?