Titlebook: ;

群岛

https://doi.org/10.1007/978-3-662-53419-9l structures (i.e. trends, patchiness and random fluctuations). A common characteristic of animal count data is a distribution that is both zero-inflated and heavy tailed. In such cases, empirical variograms are no more robust and most structural analyses result in poor and noisy estimated spatial v

HUMP

GEST

加强防卫

Meldepflicht von Erbkrankheiten,lds using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integra

疾驰

共同时代

,Die Drüsen mit innerer Sekretion,gn, performed by a measuring/recording appliance installed in a bus that maneuvered through a given path (established . in such a way that spatial variability within the city could be accounted for). In order to model such data by geostatistical techniques, a two step approach was adopted. The first

deriver

https://doi.org/10.1007/978-3-662-30072-5ire risk maps. Daily Severity Rating (DSR), a meteorological risk of forest fire index, from the Canadian Forest Fire Weather Index System (CFFWIS), results from the transformation of daily weather observations into relatively simple indices that can be used to predict fire occurrence, behaviour and

Anguish

修剪过的树篱

,Tabelle über die akuten Vergiftungen,he spatio-temporal setting. We develop a non-parametric estimator of the space–time inhomogeneous .-function. We then apply the estimator to data on the spatio-temporal distribution of human campylobacteriosis cases in an area of north-west England and investigate evidence for spatio-temporal cluste

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