A modified EM algorithm for parameter estimation in linear models with time-dependent autoregressive and t-distributed errors

authored by

Boris Kargoll, Mohammad Omidalizarandi, Hamza Alkhatib, Wolf-Dieter Schuh

Abstract

We derive an expectation conditional maximization either (ECME) algorithm for estimating jointly the parameters of a linear regression model, of a time-variable autoregressive (AR) model with respect to the random deviations, and of a scaled t-distribution with respect to the white noise components. This algorithm is shown to take the form of iteratively reweighted least squares in the estimation of the parameters both of the regression and time-variability model. The fact that the degree of freedom of that distribution is also estimated turns the algorithm into a partially adaptive estimator. As low degrees of freedom correspond to heavy-tailed distributions, the estimator can be expected to be robust against outliers. It is shown that the initial stabilization phase of an accelerometer on a shaker table can be modeled parsimoniously and robustly by a Fourier series with AR errors for which the time-variability model is defined by cubic polynomials.

Organisation(s)

Geodetic Institute

External Organisation(s)

University of Bonn

Type

Conference contribution

Pages

1132-1145

Publication date

2017

Publication status

Published

Peer reviewed

Yes

 

Details in the research portal "Research@Leibniz University"