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Semiparametric quantile regression imputation for a complex survey with application to the Conservation Effects Assessment Project

机译：用于保护效应评估项目的复杂调查的半粉末分位数回归估算

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摘要

Development of imputation procedures appropriate for data with extreme values or nonlinear relationships to covariates is a significant challenge in large scale surveys. We develop an imputation procedure for complex surveys based on semiparametric quantile regression. We apply the method to the Conservation Effects Assessment Project (CEAP), a large-scale survey that collects data used in quantifying soil loss from crop fields. In the imputation procedure, we first generate imputed values from a semiparametric model for the quantiles of the conditional distribution of the response given a covariate. Then, we estimate the parameters of interest using the generalized method of moments (WM). We derive the asymptotic distribution of the GMIVI estimators for a general class of complex survey designs. In simulations meant to represent the CEAP data, we evaluate variance estimators based on the asymptotic distribution and compare the semiparametric quantile regression imputation (QRI) method to fully parametric and nonparametric alternatives. The QRI procedure is more efficient than nonparametric and fully parametric alternatives, and empirical coverages of confidence intervals are within 1% of the nominal 95% level. An application to estimation of mean erosion indicates that QRI may be a viable option for CEAP.
机译：在适用于具有极端值或非线性关系的数据的撤销程序的开发是大规模调查中的重大挑战。基于Semiparametric Stantile回归，开发了一种用于复杂调查的估算程序。我们将该方法应用于保护效果评估项目（CEAP），这是一个大规模调查，收集量化用于量化土壤损失的数据。在归纳过程中，我们首先从给出协变量的响应的条件分布的分量中产生避税值。然后，我们使用普遍的矩（WM）来估计利息参数。我们派生了GMIVI估计的渐近分布，为一般阶级的复杂调查设计。在模拟中意味着代表CeAP数据，我们基于渐近分布评估方差估计，并将半粉末分位数回归估算（QRI）方法与全参数和非参数替代品进行比较。 QRI程序比非参数和完全参数替代品更有效，置信区间的经验覆盖率在标称95％水平的1％以内。估计平均侵蚀的应用表明QRI可能是CeAP的可行选项。

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