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

authored by
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.

Organisation(s)
Geodetic Institute
External Organisation(s)
Clausthal University of Technology
Anhalt University of Applied Sciences
Type
Contribution to book/anthology
Pages
93-99
No. of pages
7
Publication date
2024
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
Computers in Earth Sciences, Geophysics
Electronic version(s)
https://doi.org/10.1007/1345_2023_210 (Access: Open)
 

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