Journal Article

Regression models on Riemannian symmetric spaces

Cornea, Emil, Kim, Peter, Ibrahim, Joseph G., Zhu, Hongtu

2017

Overview

The paper develops a general regression framework for the analysis of manifold-valued response in a Riemannian symmetric space (RSS) and its association with multiple covariates of interest, such as age or gender, in Euclidean space. Such RSS-valued data arise frequently in medical imaging, surface modelling and computer vision, among many other fields. We develop an intrinsic regression model solely based on an intrinsic conditional moment assumption, avoiding specifying any parametric distribution in RSS. We propose various link functions to map from the Euclidean space of multiple covariates to the RSS of responses. We develop a two-stage procedure to calculate the parameter estimates and determine their asymptotic distributions. We construct the Wald and geodesic test statistics to test hypotheses of unknown parameters. We systematically investigate the geometric invariant property of these estimates and test statistics. Simulation studies and a real data analysis are used to evaluate the finite sample properties of our methods.

• Publisher: John Wiley & Sons Ltd,Oxford University Press
• Subject: age / computer vision / Data analysis / equations / Estimates / Euclidean geometry / Euclidean space / gender / Generalized method of moment / Geodesic / Group action / image analysis / Lie group / Link function / Mathematical analysis / Mathematical models / Medical imaging / Parameters / Property / Regression / regression analysis / Riemannian symmetric space / Simulation / Statistical methods / Statistical tests / Statistics / Studies / Symmetry / Tests