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 is modeled as multivariate time series stationary autoregressive (VAR) process t-distribution for white noise. Bayes’ theorem integrates prior knowledge. Parameters, including functional, VAR coefficients, scaling, degree of freedom the t-distribution, are estimated Markov Chain Monte Carlo Metropolis-within-Gibbs algorithm.