Spatio-temporal models on the basis of innovation processes and application to cancer mortality data - Ulrike Schach (Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, 2000)
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.
Keywords: 519; Lattice data; conditional autoregressive approach; spatio-temporal linear model; innovation process; ML-estimation