调整误分类：一种三阶段采样方法
Adjusting for Misclassification: A Three-Phase Sampling Approach
摘要：
美国农业部国家农业统计局（NASS）每年进行一次6月农业调查（JAS）。在预筛选过程中以及在对农场状态进行现场估计时，由于无响应和无法访问记录，发生了大量错误分类，导致JAS对美国农场数量的估计存在偏差。在这里，年度土地利用调查（ALUS）被提议作为JAS的后续调查，以调整美国农场数量和其他重要变量的估计。针对JAS-ALUS模型，提出了一种基于调查设计的三阶段估计方法，第二阶段为无响应调整。以显式形式给出了方差的设计无偏估计量.
英文摘要：
The United States Department of Agriculture's National Agricultural Statistics Service (NASS) conducts the June Agricultural Survey (JAS) annually. Substantial misclassification occurs during the pre-screening process and from field-estimating farm status for non-response and inaccessible records, resulting in a biased estimate of the number of US farms from the JAS. Here the Annual Land Utilization Survey (ALUS) is proposed as a follow-on survey to the JAS to adjust the estimates of the number of US farms and other important variables. A three-phase survey design-based estimator is developed for the JAS-ALUS with non-response adjustment for the second phase (ALUS). A design-unbiased estimator of the variance is provided in explicit form.
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