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Dokumenttyp:
Zeitungsartikel 
Autor(en):
Kreuzer, A., Erhardt, T., Nagler, T., and Czado, C. 
Titel:
Heavy tailed spatial autocorrelation models 
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...    »
 
Stichworte:
Simultaneous autoregressive model, spatial dependence, fire danger, heavy tails 
Zeitschriftentitel:
Preprint 
Jahr:
2017 
WWW:
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TUM Einrichtung:
Lehrstuhl für Mathematische Statistik 
Format:
Text