Bayesian Robust Multivariate Time Series Analysis in Nonlinear Regression Models with Vector Autoregressive and t-Distributed Errors

Alexander Dorndorf; Boris Kargoll; Jens-André Paffenholz; Hamza Alkhatib

doi:10.1007/1345_2023_210

Details

Originalsprache	Englisch
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 - 2023
Veranstaltung	X Hotine-Marussi Symposium on Mathematical Geodesy - Milan, Italien Dauer: 13 Juni 2022 → 17 Juni 2022 Konferenznummer: 10

Publikationsreihe

Name	International Association of Geodesy Symposia
Band	155
ISSN (Print)	0939-9585
ISSN (elektronisch)	2197-9359

Abstract

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.

ASJC Scopus Sachgebiete

Erdkunde und Planetologie (insg.)
Computer in den Geowissenschaften
Erdkunde und Planetologie (insg.)
Geophysik

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Bayesian Robust Multivariate Time Series Analysis in Nonlinear Regression Models with Vector Autoregressive and t-Distributed Errors. / Dorndorf, Alexander; Kargoll, Boris; Paffenholz, Jens-André et al.
X Hotine-Marussi Symposium on Mathematical Geodesy: Proceedings of the Symposium, 2022. Hrsg. / Jeffrey T. Freymueller; Laura Sánchez. Berlin, Heidelberg, 2023. S. 93-99 (International Association of Geodesy Symposia; Band 155).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Dorndorf, A, Kargoll, B, Paffenholz, J-A & Alkhatib, H 2023, Bayesian Robust Multivariate Time Series Analysis in Nonlinear Regression Models with Vector Autoregressive and t-Distributed Errors. in JT Freymueller & L Sánchez (Hrsg.), X Hotine-Marussi Symposium on Mathematical Geodesy: Proceedings of the Symposium, 2022. International Association of Geodesy Symposia, Bd. 155, Berlin, Heidelberg, S. 93-99, X Hotine-Marussi Symposium on Mathematical Geodesy, Milan, Italien, 13 Juni 2022. https://doi.org/10.1007/1345_2023_210

Dorndorf, A., Kargoll, B., Paffenholz, J.-A., & Alkhatib, H. (2023). Bayesian Robust Multivariate Time Series Analysis in Nonlinear Regression Models with Vector Autoregressive and t-Distributed Errors. In J. T. Freymueller, & L. Sánchez (Hrsg.), X Hotine-Marussi Symposium on Mathematical Geodesy: Proceedings of the Symposium, 2022 (S. 93-99). (International Association of Geodesy Symposia; Band 155).. https://doi.org/10.1007/1345_2023_210

Dorndorf A, Kargoll B, Paffenholz JA, Alkhatib H. Bayesian Robust Multivariate Time Series Analysis in Nonlinear Regression Models with Vector Autoregressive and t-Distributed Errors. in Freymueller JT, Sánchez L, Hrsg., X Hotine-Marussi Symposium on Mathematical Geodesy: Proceedings of the Symposium, 2022. Berlin, Heidelberg. 2023. S. 93-99. (International Association of Geodesy Symposia). Epub 2023 Sep 6. doi: 10.1007/1345_2023_210

Dorndorf, Alexander ; Kargoll, Boris ; Paffenholz, Jens-André et al. / Bayesian Robust Multivariate Time Series Analysis in Nonlinear Regression Models with Vector Autoregressive and t-Distributed Errors. X Hotine-Marussi Symposium on Mathematical Geodesy: Proceedings of the Symposium, 2022. Hrsg. / Jeffrey T. Freymueller ; Laura Sánchez. Berlin, Heidelberg, 2023. S. 93-99 (International Association of Geodesy Symposia).

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title = "Bayesian Robust Multivariate Time Series Analysis in Nonlinear Regression Models with Vector Autoregressive and t-Distributed Errors",

abstract = "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{\textquoteright} 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.",

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author = "Alexander Dorndorf and Boris Kargoll and Jens-Andr{\'e} Paffenholz and Hamza Alkhatib",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2023.; X Hotine-Marussi Symposium on Mathematical Geodesy, HMS 2022 ; Conference date: 13-06-2022 Through 17-06-2022",

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AU - Dorndorf, Alexander

AU - Kargoll, Boris

AU - Paffenholz, Jens-André

AU - Alkhatib, Hamza

N1 - Conference code: 10

PY - 2023

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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

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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 -

Research@Leibniz University

Bayesian Robust Multivariate Time Series Analysis in Nonlinear Regression Models with Vector Autoregressive and t-Distributed Errors

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