Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/77266
Authors: 
Schach, Ulrike
Year of Publication: 
2000
Series/Report no.: 
Technical Report, SFB 475: Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund 2000,16
Abstract: 
The aim of this paper is to find a modeling approach for spatially and temporally structured data. The spatial distribution is considered to form an irregular lattice with a specified definition of neighborhood. Additional to the spatial component, a temporal autoregressive parameter, and a time trend are modeled within a multivariates Markov process. This Markov process can be expressed on the basis of an innovation process, which allows for statistical inference on various parameters.
Subjects: 
Lattice data
conditional autoregressive approach
spatio-temporal linear model
innovation process
ML-estimation
Document Type: 
Working Paper

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