Abstract
As well as in the case of independence and by paralleling, in some sense, what happens in time series analysis, in spatial linear models the presence of anomalous observations can badly affect likelihood based inference, both on the significance of any large scale parameter and the strength of the spatial dependence. In this paper we look for a valuable robust procedure which, on the one hand, allows us to take into account possible departures of the data from the specified model, and on the other hand, can help in identifying spatial outliers. This procedure is based on weighted likelihood methodology. The effectiveness of the proposed procedure is illustrated through a small simulation study and a real data example.
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Acknowledgements
The authors wish to thank Claudio Agostinelli for helpful discussion.
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Gaetan, C., Greco, L. (2010). Weighted Likelihood Inference for a Mixed Regressive Spatial Autoregressive Model. In: Palumbo, F., Lauro, C., Greenacre, M. (eds) Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03739-9_46
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DOI: https://doi.org/10.1007/978-3-642-03739-9_46
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