Conditional Autoregressive (CAR) Model
Conditional Autoregressive (CAR) Model
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DOI：
10.1002/9781118445112.stat08048
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
A. M. Schmidt;Widemberg S. Nobre
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文献类型：
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作者：
A. M. Schmidt;Widemberg S. Nobre
条件自回归(CAR)模型用于获得基于单变量条件规范的随机向量的多变量联合分布。这些条件规范基于马尔可夫性质，使得随机向量的分量的条件分布仅依赖于一组邻居。条件自回归模型是马尔可夫随机场的特例。CAR模型已经应用于不同的科学领域；例如图像分析、流行病学和农业。通常，对于面层数据，高斯CAR规范被用作层次模型的潜在结构。在这种情况下，感兴趣的区域被划分为一组不相交的区域，并且使用CAR随机效应来解释跨不同区域进行的观测之间的可能相关性。
Conditional autoregressive (CAR) models are useful to obtain a multivariatejoint distributionsof a random vector based on univariate conditional specifications. These conditional specifications are based on Markovian properties such that the conditional distribution of a component of the random vector depends only on a set of neighbors. Conditional autoregressive models are particular cases of Markov random fields. CAR models have been applied in different areas of science; some examples are image analysis, epidemiology, and agriculture. Typically, Gaussian CAR specifications are used as latent structures inhierarchical modelsfor areal level data. In this case, the region of interest is divided into a set of disjoint areas and a CAR random effect is used to account for possible correlation among observations made across the different areas.