copCAR: A Flexible Regression Model for Areal Data.
copCAR: A Flexible Regression Model for Areal Data.
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DOI：
10.1080/10618600.2014.948178
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发表时间：
2015-09-16
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通讯作者：
Hughes J
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作者：
Hughes J
非高斯空间数据在许多领域都很常见。当拟合这些数据的回归时，需要考虑空间依赖性，以确保回归系数的可靠推断。空间聚集数据最常用的两种回归模型是自动模型和面积广义线性混合模型（GLMM）。这些模型以不同的方式诱导空间依赖性，但共享平滑方法，这是直观的，但有问题。本文提出了一种新的面积数据回归模型。新模型被称为copCAR，因为它是基于copula的，并采用了区域GLMM的条件自回归（CAR）。copCAR克服了automodel和areal GLMM的许多缺点。具体而言，copCAR（1）是灵活和直观的，（2）允许所有类型的数据的正空间依赖性，（3）允许有效的计算，（4）提供可靠的空间回归推断和有关依赖强度的信息。R软件包copCAR提供了一个实现，可以从Comprehensive R Archive Network获得，补充材料可以在线获得。
Non-Gaussian spatial data are common in many fields. When fitting regressions for such data, one needs to account for spatial dependence to ensure reliable inference for the regression coefficients. The two most commonly used regression models for spatially aggregated data are the automodel and the areal generalized linear mixed model (GLMM). These models induce spatial dependence in different ways but share the smoothing approach, which is intuitive but problematic. This article develops a new regression model for areal data. The new model is called copCAR because it is copula-based and employs the areal GLMM’s conditional autoregression (CAR). copCAR overcomes many of the drawbacks of the automodel and the areal GLMM. Specifically, copCAR (1) is flexible and intuitive, (2) permits positive spatial dependence for all types of data, (3) permits efficient computation, and (4) provides reliable spatial regression inference and information about dependence strength. An implementation is provided by R package copCAR, which is available from the Comprehensive R Archive Network, and supplementary materials are available online.