Further Results on a Modified EM Algorithm for Parameter Estimation in Linear Models with Time-Dependent Autoregressive and t-Distributed Errors

authored by

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.

Organisation(s)

Geodetic Institute

External Organisation(s)

University of Bonn

Type

Conference contribution

Pages

323-337

No. of pages

14

Publication date

04.10.2018

Publication status

Published

Peer reviewed

Yes

Electronic version(s)

https://doi.org/10.1007/978-3-319-96944-2_22 (Access: Closed)

 

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