Stochastic development regression using method of moments

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This paper considers the estimation problem arising when inferring parameters in the stochastic development regression model for manifold valued non-linear data. Stochastic development regression captures the relation between manifold-valued response and Euclidean covariate variables using the stochastic development construction. It is thereby able to incorporate several covariate variables and random effects. The model is intrinsically defined using the connection of the manifold, and the use of stochastic development avoids linearizing the geometry. We propose to infer parameters using the Method of Moments procedure that matches known constraints on moments of the observations conditional on the latent variables. The performance of the model is investigated in a simulation example using data on finite dimensional landmark manifolds.

Original languageEnglish
Title of host publicationGeometric Science of Information : Third International Conference, GSI 2017, Paris, France, November 7-9, 2017, Proceedings
EditorsFrank Nielsen, Fréderic Barbaresco
Number of pages9
PublisherSpringer
Publication date2017
Pages3-11
ISBN (Print)978-3-319-68444-4
ISBN (Electronic)978-3-319-68445-1
DOIs
Publication statusPublished - 2017
Event3rd International Conference on Geometric Science of Information - Paris, France
Duration: 7 Nov 20179 Nov 2017
Conference number: 3

Conference

Conference3rd International Conference on Geometric Science of Information
Nummer3
LandFrance
ByParis
Periode07/11/201709/11/2017
SeriesLecture notes in computer science
Volume10589
ISSN0302-9743

    Research areas

  • Frame bundle, Non-linear statistics, Regression, Statistics on manifolds, Stochastic development

ID: 188481061