Ferrándiz J, López A, Llopis A, Morales M, Tejerizo M L
Departamento de Estadística e I.O., Universitat de València, Burjassot, Spain.
Biometrics. 1995 Jun;51(2):665-78.
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.
地理死亡率数据的统计分析通常通过包含适当协变量的回归模型来进行。这些模型假定相邻地区死亡率计数的随机独立性,而空间自模型(贝萨格,1974年,《皇家统计学会学报》,B辑36卷,192 - 236页)认为这是一个值得怀疑的假设,且没有必要。本文介绍了使用自泊松分布来检测相邻地区之间的空间相互作用。如果这种相互作用结果不显著,自泊松分布就简化为一个普通的泊松回归模型,这是广义线性模型(麦卡拉和内尔德,1989年,《广义线性模型》,第2版。伦敦:查普曼与霍尔出版社)的一个特殊情况,可以用GLIM软件包进行分析。