Details
| Original language | English |
|---|---|
| Title of host publication | X Hotine-Marussi Symposium on Mathematical Geodesy |
| Subtitle of host publication | Proceedings of the Symposium, 2022 |
| Editors | Jeffrey T. Freymueller, Laura Sánchez |
| Place of Publication | Berlin, Heidelberg |
| Pages | 93-99 |
| Number of pages | 7 |
| Publication status | Published - 2023 |
| Event | X Hotine-Marussi Symposium on Mathematical Geodesy - Milan, Italy Duration: 13 Jun 2022 → 17 Jun 2022 Conference number: 10 |
Publication series
| Name | International Association of Geodesy Symposia |
|---|---|
| Volume | 155 |
| ISSN (Print) | 0939-9585 |
| ISSN (electronic) | 2197-9359 |
Abstract
Keywords
- Metropolis-within-Gibbs algorithm, Robust Bayesian time series analysis, VAR process, t-distribution
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Earth and Planetary Sciences(all)
- Geophysics
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X Hotine-Marussi Symposium on Mathematical Geodesy: Proceedings of the Symposium, 2022. ed. / Jeffrey T. Freymueller; Laura Sánchez. Berlin, Heidelberg, 2023. p. 93-99 (International Association of Geodesy Symposia; Vol. 155).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
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 - Conference code: 10
PY - 2023
Y1 - 2023
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 - Conference contribution
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
T2 - X Hotine-Marussi Symposium on Mathematical Geodesy
Y2 - 13 June 2022 through 17 June 2022
ER -