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A Bayesian Nonlinear Regression Model Based on t-Distributed Errors

verfasst von
Alexander Dorndorf, Boris Kargoll, Jens André Paffenholz, Hamza Alkhatib
Abstract

In this contribution, a robust Bayesian approach to adjusting a nonlinear regression model with t-distributed errors is presented. In this approach the calculation of the posterior model parameters is feasible without linearisation of the functional model. Furthermore, the integration of prior model parameters in the form of any family of prior distributions is demonstrated. Since the posterior density is then generally non-conjugated, Monte Carlo methods are used to solve for the posterior numerically. The desired parameters are approximated by means of Markov chain Monte Carlo using Gibbs samplers and Metropolis-Hastings algorithms. The result of the presented approach is analysed by means of a closed-loop simulation and a real world application involving GNSS observations with synthetic outliers.

Organisationseinheit(en)
Geodätisches Institut
Typ
Aufsatz in Konferenzband
Seiten
127-135
Anzahl der Seiten
9
Publikationsdatum
2019
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
ASJC Scopus Sachgebiete
Computer in den Geowissenschaften, Geophysik
Elektronische Version(en)
https://doi.org/10.1007/1345_2019_76 (Zugang: Geschlossen)
 

Details im Forschungsportal „Research@Leibniz University“