Spatially explicit survival modeling for small area cancer data
In this paper we propose a novel Bayesian statistical methodology for spatial survival data. Our methodology broadens the definition of the survival, density and hazard functions by explicitly modeling the spatial dependency using direct derivations of these functions and their marginals and conditi...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | Journal of applied statistics. - 1991. - 45(2018), 3 vom: 15., Seite 568-585
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| 1. Verfasser: |
Onicescu, G
(VerfasserIn) |
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| Weitere Verfasser: |
Lawson, A,
Zhang, J,
Gebregziabher, Mulugeta,
Wallace, Kristin,
Eberth, J M |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2018
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| Zugriff auf das übergeordnete Werk: | Journal of applied statistics
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| Schlagworte: | Journal Article
Bayesian hierarchical models
Markov chain Monte Carlo
kernel convolution
prostate cancer
spatial |
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