Spatio-Temporal Models on the Basis of Innovation Processes and Application to Cancer Mortality Data

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Date

2000

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Volume Title

Publisher

Universitätsbibliothek Dortmund

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.

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Keywords

conditional autoregressive approach, innovation process, lattice data, ML-estimation, spatio-temporal linear model

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