Inference in Gaussian models with missing data using Equalisation Maximisation
Abstract
Equalisation Maximisation (EqM) is an algorithm for estimating parameters in auto-regressive (AR) models where some fraction of the data is missing. It has previously been shown that the EqM algorithm is a competitive alternative to expectation maximisation, estimating models with equal predictive capability at a lower computational cost. The EqM algorithm has previously been motivated as a heuristic. In this paper, we instead show that EqM can be viewed as an approximation of a proximal point algorithm. We also derive the method for the entire class of Gaussian models and exemplify its use for estimation of ARMA models with missing data. The resulting method is evaluated in numerical simulations, resulting in similar results as for the AR processes.
Cite
@article{arxiv.1308.4601,
title = {Inference in Gaussian models with missing data using Equalisation Maximisation},
author = {Johan Dahlin and Fredrik Lindsten and Thomas B. Schön},
journal= {arXiv preprint arXiv:1308.4601},
year = {2013}
}