Details
Originalsprache | Englisch |
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Titel des Sammelwerks | X Hotine-Marussi Symposium on Mathematical Geodesy |
Untertitel | Proceedings of the Symposium, 2022 |
Herausgeber/-innen | Jeffrey T. Freymueller, Laura Sánchez |
Erscheinungsort | Berlin, Heidelberg |
Seiten | 93-99 |
Seitenumfang | 7 |
Publikationsstatus | Veröffentlicht - 2024 |
Publikationsreihe
Name | International Association of Geodesy Symposia |
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Band | 155 |
ISSN (Print) | 0939-9585 |
ISSN (elektronisch) | 2197-9359 |
Abstract
ASJC Scopus Sachgebiete
- Erdkunde und Planetologie (insg.)
- Computer in den Geowissenschaften
- Erdkunde und Planetologie (insg.)
- Geophysik
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X Hotine-Marussi Symposium on Mathematical Geodesy: Proceedings of the Symposium, 2022. Hrsg. / Jeffrey T. Freymueller; Laura Sánchez. Berlin, Heidelberg, 2024. S. 93-99 (International Association of Geodesy Symposia; Band 155).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - Bayesian Robust Multivariate Time Series Analysis in Nonlinear Regression Models with Vector Autoregressive and t-Distributed Errors
AU - Dorndorf, Alexander
AU - Kargoll, Boris
AU - Paffenholz, Jens-André
AU - Alkhatib, Hamza
N1 - Publisher Copyright: © The Author(s) 2023.
PY - 2024
Y1 - 2024
N2 - Geodetic measurements rely on high-resolution sensors, but produce data sets with many observations which may contain outliers and correlated deviations. This paper proposes a powerful solution using Bayesian inference. The observed data is modeled as a multivariate time series with a stationary autoregressive (VAR) process and multivariate t-distribution for white noise. Bayes’ theorem integrates prior knowledge. Parameters, including functional, VAR coefficients, scaling, and degree of freedom of the t-distribution, are estimated with Markov Chain Monte Carlo using a Metropolis-within-Gibbs algorithm.
AB - Geodetic measurements rely on high-resolution sensors, but produce data sets with many observations which may contain outliers and correlated deviations. This paper proposes a powerful solution using Bayesian inference. The observed data is modeled as a multivariate time series with a stationary autoregressive (VAR) process and multivariate t-distribution for white noise. Bayes’ theorem integrates prior knowledge. Parameters, including functional, VAR coefficients, scaling, and degree of freedom of the t-distribution, are estimated with Markov Chain Monte Carlo using a Metropolis-within-Gibbs algorithm.
KW - Metropolis-within-Gibbs algorithm
KW - Robust Bayesian time series analysis
KW - VAR process
KW - t-distribution
UR - http://www.scopus.com/inward/record.url?scp=85195465959&partnerID=8YFLogxK
U2 - 10.1007/1345_2023_210
DO - 10.1007/1345_2023_210
M3 - Contribution to book/anthology
SN - 9783031553592
T3 - International Association of Geodesy Symposia
SP - 93
EP - 99
BT - X Hotine-Marussi Symposium on Mathematical Geodesy
A2 - Freymueller, Jeffrey T.
A2 - Sánchez, Laura
CY - Berlin, Heidelberg
ER -