作为广义线性混合模型的泊松协同克里金法
POISSON COKRIGING AS A GENERALIZED LINEAR MIXED MODEL.
作者信息
Smith Lynette M, Stroup Walter W, Marx David B
机构信息
Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, 984375 Nebraska Medical Center, Omaha, NE 68198-4375, USA.
Department of Statistics, University of Nebraska-Lincoln, 340 Hardin Hall North Wing, Lincoln, NE 68583-0963, USA.
出版信息
Spat Stat. 2020 Mar;35. doi: 10.1016/j.spasta.2019.100399. Epub 2019 Dec 13.
It is often of interest to predict spatially correlated count outcomes that follow a Poisson distribution. For example, in the environmental sciences we may want to predict pollen counts using temperature or precipitation data as auxiliary variables. To predict a Poisson outcome variable in the presence of an auxiliary variable, Poisson cokriging as a Generalized Linear Mixed Model (GLMM) is proposed. This model has a bivariate structure with a Poisson outcome variable and an auxiliary variable. A covariance matrix similar to that used in cokriging is assumed. A simulation study and a real data example using the number of microplastics in the digestive tracts of fish are presented. The results showed that Poisson cokriging methodology can be applied successfully in practice with small average errors and coverage close to 95%. The Poisson cokriging model can be a useful tool for spatial prediction.
预测服从泊松分布的空间相关计数结果通常很有意义。例如，在环境科学中，我们可能想用温度或降水数据作为辅助变量来预测花粉计数。为了在存在辅助变量的情况下预测泊松结果变量，提出了作为广义线性混合模型（GLMM）的泊松协同克里金法。该模型具有一个包含泊松结果变量和一个辅助变量的二元结构。假定使用与协同克里金法中类似的协方差矩阵。给出了一个模拟研究以及一个使用鱼消化道中微塑料数量的实际数据示例。结果表明，泊松协同克里金法在实践中能够成功应用，平均误差较小且覆盖率接近95%。泊松协同克里金模型可以成为空间预测的一个有用工具。
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