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Robust estimation methods for a class of log-linear count time series models

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>2016
<mark>Journal</mark>Journal of Statistical Computation and Simulation
Issue number4
Number of pages16
Pages (from-to)740-755
Publication StatusPublished
Early online date21/04/15
<mark>Original language</mark>English


We study robust estimation of a log-linear Poisson model for count time series analysis. More specifically, we study robust versions of maximum likelihood estimators (MLEs) under three different forms of interventions: additive outliers (AOs), transient shifts (TSs) and level shifts (LSs). We estimate the parameters using the MLE, the conditionally unbiased bounded-influence estimator and the Mallows quasi-likelihood estimator and compare all three estimators in terms of their mean-square error, bias and mean absolute error. Our empirical results illustrate that under a LS or a TS there are no significant differences among the three estimators and the most interesting results are obtained in the presence of AOs. The results are complemented by a real data example.