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Spatial Dependency Modeling of Daily Mean Temperature Time Series using Spatial R-vine Models

TitleSpatial Dependency Modeling of Daily Mean Temperature Time Series using Spatial R-vine Models
Publication TypeConference Paper
Year of Publication2014
AuthorsErhardt TMichael, Czado C, Schepsmeier U
Conference NameDailyMeteo.org/2014
Date Published06/2014
PublisherFaculty of Civil Engineering, University of Belgrade
Conference LocationBelgrade
Abstract

Classical geostatistical approaches for the modeling of spatial dependencies assume a Gaussian dependency structure. This assumption may simplify the modeling process, however it is not always met, when we face real world problems. We are going to introduce a new parametric R-vine copula based modeling approach, which is able to capture non-Gaussian spatial dependencies. R-vine copulas are a class of flexible multi-dimensional probability distributions, composed out of bivariate copulas. For each of these bivariate building blocks we can choose among a variety of different dependency structures (copula families), which are well understood and easy to compute. The proposed "spatial R-vine model" combines the flexibility of vine copulas with the geostatistical idea of modeling spatial dependencies by means of the distances between the variable locations. To illustrate the model development process we consider daily mean temperature time series taken from 54 monitoring stations across Germany.

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