Calibration with Estimated Controls

从调查中获得的结果被用来估计人口的特征。校准估计器使用辅助信息来提高这些调查估计的效率。 在美国，校准被用于人口普查局的当前人口调查（CPS）、劳工统计局的消费者支出调查、各种其他联邦调查以及其他国家管理的调查。 校准的一些主要用途是（i）减少方差，（ii）校正无响应，（iii）校正样本框架的覆盖率不足。校准的主要假设是样本估计值被校准到没有错误的真实总体值。然而，这些总体值通常是从其他调查中获得的估计值，这些调查可能具有抽样方差。这些估计的控制增加了调查估计的可变性，如果控制来源不准确，可能会产生偏差。在调查中使用估计控制是常见的，但实际上从未在调查方差估计中考虑过。本研究将调查解释方差估计中这种额外不精确性的方法，并研究总体值和分析调查本身中偏差的影响。本研究的具体目标是：（1）分析基准和分析调查估计的影响，由于覆盖误差与一般回归（GREG）估计和其他校准估计的扩展;（2）开发GREG和其他校准估计的方差估计，说明估计的基准和样本框架的覆盖不足;（3）制定一些实用规则，以确定估计的基准是否变化太大而不值得使用;（4）通过基于真实的调查数据集的模拟研究来说明理论发现。

The results obtained from surveys are used to estimate characteristics of populations. Calibration estimators use auxiliary information to improve the efficiency of these survey estimates. Calibration is used in the U.S. in the Census Bureau's Current Population Survey (CPS), the Bureau of Labor Statistics' Consumer Expenditure Survey, and a variety of other Federal surveys as well as surveys administered by other nations. Some of the key uses of calibration are to (i) reduce variances, (ii) correct for nonresponse, and (iii) correct coverage deficiencies of a sample frame.A primary assumption with calibration is that the sample estimates are calibrated to true population values known without error. Often, however, these population values are estimates obtained from other surveys which could possess sampling variance. These estimated controls add variability to survey estimates and may produce bias if the source of controls is inaccurate. The use of estimated controls in surveys is common but is virtually never accounted for in survey variance estimation. This research will investigate methods that will account for this extra imprecision in variance estimates and also study the effects of bias in the population vlues and in the analytic survey itself. The specific goals of this research will be to (1) analyze the effect of benchmarks and analytic survey estimates that are biased due to coverage errors with extensions done for general regression (GREG) estimators and other calibration estimators; (2) develop variance estimators for GREG and other calibration estimators that account for estimated benchmarks and undercoverage by sample frames; (3) develop some practical rules for determining whether estimated benchmarks are too variable to be worth using; and (4) illustrate theoretical findings via simulation studies based on real survey data sets.