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

1-7

No. of pages

7

Publication date

06.09.2023

Publication status

E-pub ahead of print

Peer reviewed

Yes

Electronic version(s)

https://doi.org/10.1007/1345_2023_210 (Access: Open)

 

Details in the research portal "Research@Leibniz University"