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

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DC Field	Value	Language
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.identifier.uri	http://hdl.handle.net/2003/5021	-
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15113	-
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.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	-
Appears in Collections:	Sonderforschungsbereich (SFB) 475

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