Unknown

Dataset Information

0

Regression Models on Riemannian Symmetric Spaces.


ABSTRACT: The aim of this paper is to develop 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 arises frequently in medical imaging, surface modeling, and computer vision, among many others. 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.

SUBMITTER: Cornea E 

PROVIDER: S-EPMC5433528 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

altmetric image

Publications

Regression Models on Riemannian Symmetric Spaces.

Cornea Emil E   Zhu Hongtu H   Kim Peter P   Ibrahim Joseph G JG  

Journal of the Royal Statistical Society. Series B, Statistical methodology 20160320 2


The aim of this paper is to develop 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 arises frequently in medical imaging, surface modeling, and computer vision, among many others. We develop an intrinsic regression model solely based on an intrinsic conditional moment assumption, avoiding specifying any paramet  ...[more]

Similar Datasets

| S-EPMC5378241 | biostudies-other
| S-EPMC10757968 | biostudies-literature
| S-EPMC3448376 | biostudies-literature
| S-EPMC4288036 | biostudies-literature
| S-EPMC3867140 | biostudies-literature
| S-EPMC7868056 | biostudies-literature
| S-EPMC6214282 | biostudies-literature
| S-EPMC4155339 | biostudies-literature
| S-EPMC5365157 | biostudies-literature
| S-EPMC8494047 | biostudies-literature