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• W2030714634 abstract "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." @default.
• W2030714634 created "2016-06-24" @default.
• W2030714634 creator A5005421273 @default.
• W2030714634 date "2015-07-03" @default.
• W2030714634 modified "2023-09-23" @default.
• W2030714634 title "copCAR: A Flexible Regression Model for Areal Data" @default.
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• W2030714634 doi "https://doi.org/10.1080/10618600.2014.948178" @default.
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