Literature DB >> 7662853

Spatial interaction between neighbouring counties: cancer mortality data in Valencia Spain.

J Ferrándiz1, A López, A Llopis, M Morales, M L Tejerizo.   

Abstract

The statistical analysis of geographical mortality data has usually been approached via regression models that include appropriate covariates. These models assume stochastic independence of mortality counts for neighbouring sites, a questionable assumption that spatial automodels (Besag, 1974, Journal of the Royal Statistical Society, Series B 36, 192-236) make unnecessary. This paper presents the use of the autopoisson distribution in order to detect spatial interaction between neighbouring sites. If this interaction results in being nonsignificant, the auto-Poisson distribution reduces to a usual Poisson regression model, a particular case of generalized linear models (McCullagh and Nelder, 1989, Generalized Linear Models, 2nd edition. London: Chapman and Hall) which can be analyzed with the GLIM package.

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Year:  1995        PMID: 7662853

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  3 in total

1.  Exploratory analysis and a stochastic model for humusdisintegration.

Authors:  R Fried; J Eichhorn; U Paar
Journal:  Environ Monit Assess       Date:  2001-05       Impact factor: 2.513

2.  A modified version of Moran's I.

Authors:  Monica C Jackson; Lan Huang; Qian Xie; Ram C Tiwari
Journal:  Int J Health Geogr       Date:  2010-06-29       Impact factor: 3.918

3.  Spatial distribution of the relative risk of Zika virus disease in Colombia during the 2015-2016 epidemic from a Bayesian approach.

Authors:  Karen Flórez-Lozano; Edgar Navarro-Lechuga; Humberto Llinás-Solano; Rafael Tuesca-Molina; Augusto Sisa-Camargo; Marcela Mercado-Reyes; Martha Ospina-Martínez; Franklyn Prieto-Alvarado; Jorge Acosta-Reyes
Journal:  Int J Gynaecol Obstet       Date:  2020-01       Impact factor: 3.561

  3 in total

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