Prediction of small area quantiles for the conservation effects assessment project using a mixed effects quantile regression model
Prediction of small area quantiles for the conservation effects assessment project using a mixed effects quantile regression model
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DOI：
10.1214/19-aoas1276
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Emily J. Berg;Danhyang Lee
中科院分区：
文献类型：
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作者：
Emily J. Berg;Danhyang Lee
几种侵蚀测量值的分布分位数是保护效果评估项目的重要参数，该项目旨在量化农田土壤和养分流失的调查。由于感兴趣领域的样本量太小而无法支持可靠的直接估计器，因此需要基于模型的方法。分位数回归对 CEAP 很有吸引力，因为找到一个能够充分描述所有变量分布的参数模型非常困难，而且小面积分位数是感兴趣的参数。我们基于线性插值广义帕累托分布（LIGPD）构建经验贝叶斯预测器和自举均方误差估计器。我们应用该程序来预测威斯康星州四种侵蚀类型的县级分位数，并通过模拟验证该程序。
Quantiles of the distributions of several measures of erosion are important parameters in the Conservation Effects Assessment Project, a survey intended to quantify soil and nutrient loss on crop fields. Because sample sizes for domains of interest are too small to support reliable direct estimators, model based methods are needed. Quantile regression is appealing for CEAP because finding a single family of parametric models that adequately describes the distributions of all variables is difficult and small area quantiles are parameters of interest. We construct empirical Bayes predictors and bootstrap mean squared error estimators based on the linearly interpolated generalized Pareto distribution (LIGPD). We apply the procedures to predict county-level quantiles for four types of erosion in Wisconsin and validate the procedures through simulation.