Further Results on a Modified EM Algorithm for Parameter Estimation in Linear Models with Time-Dependent Autoregressive and t-Distributed Errors
- verfasst von
- Boris Kargoll, Hamza Alkhatib, Mohammad Omidalizarandi, Wolf-Dieter Schuh
- Abstract
In this contribution, we consider an expectation conditional maximization either (ECME) algorithm for the purpose of estimating the parameters of a linear observation model with time-dependent autoregressive (AR) errors. The degree of freedom (d.o.f.) of the underlying family of scaled t-distributions, which is used to account for outliers and heavy-tailedness of the white noise components, is adapted to the data, resulting in a self-tuning robust estimator. The time variability of the AR coefficients is described by a second linear model. We improve the estimation of the d.o.f. in a previous version of the ECME algorithm, which involves a zero search, by using an interval Newton method. We model the transient oscillations of a shaker table measured by a high-accuracy accelerometer, and we analyze various criteria for selecting a simultaneously parsimonious and realistic time-variability model.
- Organisationseinheit(en)
-
Geodätisches Institut
- Externe Organisation(en)
-
Rheinische Friedrich-Wilhelms-Universität Bonn
- Typ
- Aufsatz in Konferenzband
- Seiten
- 323-337
- Anzahl der Seiten
- 14
- Publikationsdatum
- 04.10.2018
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- Elektronische Version(en)
-
https://doi.org/10.1007/978-3-319-96944-2_22 (Zugang:
Geschlossen)
