Title
Regression models on Riemannian symmetric spaces
Introduction
Manifold-valued responses in curved spaces frequently arise in many disciplines including medical imaging, computational biology and computer vision, among many others. For instance, in medical and molecular imaging, it is interesting to delineate the changes in the shape and anatomy of a molecule. Regression anal- ysis is a fundamental statistical tool for relating a response variable to a covariate, such as age and gender. In particular, when both the response and the covariate(s) are in Euclidean space, the classical linear regression model and its variants have been widely used in various fields. However, when the response is in a Riemannian symmetric space (RSS) and the covariates are in Euclidean space, developing regression models for this type is difficult.Questions
An intriguing question is whether there is a general regression framework for manifold-valued response in an RSS and covariates in a multi-dimensional Euclidean space. The aim of this paper is to give an affirmative answer to such a question.Methods
(a) We propose an intrinsic regression model solely based on an intrinsic conditional moment for the response in an RSS, thus avoiding specifying any parametric distributions in a general RSS—the model can handle multiple covariates in Euclidean space.
(b) We develop several ‘efficient’ estimation methods for estimating the regression coefficients in this intrinsic model.
(c) We develop several test statistics for testing linear hypotheses of the regression coefficients.
(d) We develop a general asymptotic frame work for the estimates of the regression coefficients and test statistics.
(e) We systematically investigate the geometrical properties (e.g. chart invariance) of these parameter estimates and test statistics.