Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 215 - Contributed Poster Presentations: Section on Statistical Learning and Data Science
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313619
Title: Robust Extrinsic Framework for Manifold Valued Data Analysis
Author(s): Hwiyoung Lee*
Companies: Florida State University
Keywords: Riemannian Manifold; Robust Statistics; Nonparametric Regression; Extrinsic Data Analysis

In recent years, analyzing data on manifolds has received increased attention in many statistical applications as a means to provide accurate inference based on usage of underlying geometry of the object space. Numerous robust methods have been extensively studied on Euclidean spaces, whereas much less attention has been paid to manifold data. In this paper we introduce a robust extrinsic framework for conducting manifold valued data analysis. First, by extending the notion of the geometric median in an Euclidean space to the embedded manifold case, we propose a new robust location parameter so-called extrinsic median. A robust regression method is also developed by incorporating local polynomial regression methods and the extrinsic median. We present the Weiszfeld's algorithm for computing these proposed manifold valued statistics. The promising performance of our approach against existing methods is illustrated through simulation study.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program