Prediction of small area quantiles for the conservation effects assessment project using a mixed effects quantile regression model
The Annals of Applied Statistics · 2019 · Volume 13 · Issue 4
Abstract
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.
Keywords
Empirical Bayes, environmental monitoring, erosion, Parametric bootstrap, Quantile regression
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