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Title:

Heavy tailed spatial autocorrelation models

Document type:
Zeitungsartikel
Author(s):
Kreuzer, A., Erhardt, T., Nagler, T., and Czado, C.
Abstract:
Appropriate models for spatially autocorrelated data account for the fact that observations are not independent. A popular model in this context is the simultaneous autoregressive (SAR) model that allows to model the spatial dependency structure of a response variable and the influence of covariates on this variable. This spatial regression model assumes that the error follows a normal distribution. Since this assumption cannot always be met, it is necessary to extend this model to other error d...     »
Keywords:
Simultaneous autoregressive model, spatial dependence, fire danger, heavy tails
Journal title:
Preprint
Year:
2017
Submitted:
28.08.2017
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
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