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

dc.contributor.author	Schach, Ulrike	de
dc.date.accessioned	2004-12-06T18:42:11Z
dc.date.available	2004-12-06T18:42:11Z
dc.date.issued	2000	de
dc.description.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.	en
dc.format.extent	1590717 bytes
dc.format.extent	263402 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/5021
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15113
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	conditional autoregressive approach	en
dc.subject	innovation process	en
dc.subject	lattice data	en
dc.subject	ML-estimation	en
dc.subject	spatio-temporal linear model	en
dc.subject.ddc	310	de
dc.title	Spatio-Temporal Models on the Basis of Innovation Processes and Application to Cancer Mortality Data	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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