Bayesian Robust Multivariate Time Series Analysis in Nonlinear Regression Models with Vector Autoregressive and t-Distributed Errors
- verfasst von
- Alexander Dorndorf, Boris Kargoll, Jens-André Paffenholz, Hamza Alkhatib
- 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.
- Organisationseinheit(en)
-
Geodätisches Institut
- Externe Organisation(en)
-
Technische Universität Clausthal
Hochschule Anhalt
- Typ
- Aufsatz in Konferenzband
- Seiten
- 93-99
- Anzahl der Seiten
- 7
- Publikationsdatum
- 2023
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Computer in den Geowissenschaften, Geophysik
- Elektronische Version(en)
-
https://doi.org/10.1007/1345_2023_210 (Zugang:
Offen)
