Stochastic development regression using method of moments

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Stochastic development regression using method of moments. / Kühnel, Line; Sommer, Stefan Horst.

Geometric Science of Information: Third International Conference, GSI 2017, Paris, France, November 7-9, 2017, Proceedings. red. / Frank Nielsen; Fréderic Barbaresco. Springer, 2017. s. 3-11 (Lecture notes in computer science, Bind 10589).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Kühnel, L & Sommer, SH 2017, Stochastic development regression using method of moments. i F Nielsen & F Barbaresco (red), Geometric Science of Information: Third International Conference, GSI 2017, Paris, France, November 7-9, 2017, Proceedings. Springer, Lecture notes in computer science, bind 10589, s. 3-11, 3rd International Conference on Geometric Science of Information, Paris, Frankrig, 07/11/2017. https://doi.org/10.1007/978-3-319-68445-1_1

APA

Kühnel, L., & Sommer, S. H. (2017). Stochastic development regression using method of moments. I F. Nielsen, & F. Barbaresco (red.), Geometric Science of Information: Third International Conference, GSI 2017, Paris, France, November 7-9, 2017, Proceedings (s. 3-11). Springer. Lecture notes in computer science, Bind. 10589 https://doi.org/10.1007/978-3-319-68445-1_1

Vancouver

Kühnel L, Sommer SH. Stochastic development regression using method of moments. I Nielsen F, Barbaresco F, red., Geometric Science of Information: Third International Conference, GSI 2017, Paris, France, November 7-9, 2017, Proceedings. Springer. 2017. s. 3-11. (Lecture notes in computer science, Bind 10589). https://doi.org/10.1007/978-3-319-68445-1_1

Author

Kühnel, Line ; Sommer, Stefan Horst. / Stochastic development regression using method of moments. Geometric Science of Information: Third International Conference, GSI 2017, Paris, France, November 7-9, 2017, Proceedings. red. / Frank Nielsen ; Fréderic Barbaresco. Springer, 2017. s. 3-11 (Lecture notes in computer science, Bind 10589).

Bibtex

@inproceedings{18fd4b9f334b4b8f9f0ffab737da9731,
title = "Stochastic development regression using method of moments",
abstract = "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.",
keywords = "Frame bundle, Non-linear statistics, Regression, Statistics on manifolds, Stochastic development",
author = "Line K{\"u}hnel and Sommer, {Stefan Horst}",
year = "2017",
doi = "10.1007/978-3-319-68445-1_1",
language = "English",
isbn = "978-3-319-68444-4",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "3--11",
editor = "Frank Nielsen and Fr{\'e}deric Barbaresco",
booktitle = "Geometric Science of Information",

}

RIS

TY - GEN

T1 - Stochastic development regression using method of moments

AU - Kühnel, Line

AU - Sommer, Stefan Horst

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Frame bundle

KW - Non-linear statistics

KW - Regression

KW - Statistics on manifolds

KW - Stochastic development

UR - http://www.scopus.com/inward/record.url?scp=85033693692&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-68445-1_1

DO - 10.1007/978-3-319-68445-1_1

M3 - Article in proceedings

AN - SCOPUS:85033693692

SN - 978-3-319-68444-4

T3 - Lecture notes in computer science

SP - 3

EP - 11

BT - Geometric Science of Information

A2 - Nielsen, Frank

A2 - Barbaresco, Fréderic

PB - Springer

ER -

ID: 188481061