Downweighting influential clusters in surveys: Application to the 1990 Post Enumeration Survey
Downweighting influential clusters in surveys: Application to the 1990 Post Enumeration Survey
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DOI：
10.1198/016214501753208889
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发表时间：
2001-09-01
影响因子：
3.7
通讯作者：
Belin, TR
中科院分区：
文献类型：
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作者：
Zaslavsky, AM;Schenker, N;Belin, TR
某些集群可能对调查估计有极大的影响，因此对它们的方差贡献不成比例，我们提出了一种通用的估计方法，该方法对具有高度影响力的集群进行降权，基于M估计的降权量应用于集群的经验影响，该方法的动机是人口普查覆盖估计中的一个问题，我们用1990年的统计调查（PES）的数据来说明这一点。在这方面，必须有一个客观的、预先规定的方法来处理有影响的意见，以避免必须证明事后对权重进行判断性调整是合理的。1990年，人口普查中的极端权重和大误差都导致了极端影响。我们采用Taylor线性化的方法估计影响，并应用基于t分布和Huber psi函数的M-估计。理论预测，稳健方法大大降低了估计覆盖率的估计方差，比权值截断更有效。另一方面，当影响统计量的分布不对称时，稳健方法可能会引入偏差。我们考虑的属性的估计存在的不对称性，我们展示了技术评估的偏差方差权衡，发现估计的均方误差减少通过应用强大的程序，我们的数据集，我们还建议PES设计的改进，以减少有影响力的集群的影响。
Certain clusters may be extremely influential on survey estimates and consequently contribute disproportionately to their variance, We propose a general approach to estimation that downweights highly influential clusters, with the amount of downweighting based on M-estimation applied to the empirical influence of the clusters, The method is motivated by a problem in census coverage estimation, and we illustrate it by using data from the 1990 Post Enumeration Survey (PES). in this context, an objective, prespecified methodology for handling influential observations is essential to avoid having to justify judgmental post hoc adjustment of weights. In 1990, both extreme weights and large errors in the census led to extreme influence. We estimated influence by Taylor linearization of the survey estimator, and we applied M-estimators based on the t distribution and the Huber psi -function, As predicted by theory, the robust procedures greatly reduced the estimated variance of estimated coverage rates, more so than did truncation of weights, On the other hand, the procedure may introduce bias into survey estimates when the distributions of the influence statistics are asymmetric. We consider the properties of the estimators in the presence of asymmetry, and we demonstrate techniques for assessing the bias-variance trade-off, finding that estimated mean squared error is reduced by applying the robust procedure to our dataset, We also suggest PES design improvements to reduce the impact of influential clusters.