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

Kitromilidou, S. and Fokianos, K. (2016) Robust estimation methods for a class of log-linear count time series models. Journal of Statistical Computation and Simulation, 86 (4). pp. 740-755. ISSN 0094-9655

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Statistical Computation and Simulation
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
127751
Deposited By:
Deposited On:
26 Sep 2018 08:14
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Jul 2020 04:37