Proper multivariate conditional autoregressive models for spatial data analysis
Proper multivariate conditional autoregressive models for spatial data analysis
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DOI：
10.1093/biostatistics/4.1.11
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发表时间：
2003-01-01
期刊：
影响因子：
2.1
通讯作者：
Vounatsou, P
中科院分区：
文献类型：
--
作者：
Gelfand, AE;Vounatsou, P
在过去的十年中，条件自回归建模规范在空间数据分析中得到了广泛的应用。几乎所有这些工作都是在单变量情况下完成的，并且使用了不正确的规范。我们在这里的贡献是转向多变量、条件自回归模型，并提供产生适当分布的丰富、灵活的类别。我们的方法是引入空间自回归参数。我们首先澄清了哪些类可以从MarDia(1988)家族中发展出来，并与Kim等人最近的工作进行了对比。(2000年)。然后，我们提出了一种新的参数线性变换，它提供了一个具有吸引人的解释的扩展。我们建议使用这些模型作为分层模型中第二阶段空间效应的规范。讨论了两种应用：一种是对儿童生长的空间模式进行二维情况模拟，另一种是对人类白细胞抗原B等位基因频率的空间变异进行四维情况模拟。在每种情况下，都使用马尔科夫链蒙特卡罗模拟进行完全贝叶斯推理。
In the past decade conditional autoregressive modelling specifications have found considerable application for the analysis of spatial data. Nearly all of this work is done in the univariate case and employs an improper specification. Our contribution here is to move to multivariate, conditional autoregressive models and to provide rich, flexible classes which yield proper distributions. Our approach is to introduce spatial autoregression parameters. We first clarify what classes can be developed from the family of Mardia (1988) and contrast with recent work of Kim et al. (2000). We then present a novel parametric linear transformation which provides an extension with attractive interpretation. We propose to employ these models as specifications for second-stage spatial effects in hierarchical models. Two applications are discussed; one for the two-dimensional case modelling spatial patterns of child growth, the other for a four-dimensional situation modelling spatial variation in HLA-B allele frequencies. In each case, full Bayesian inference is carried out using Markov chain Monte Carlo simulation.