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A method to correct for frame membership error in dual frame estimators

机译:双帧估算器中校正帧成员误差的方法

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Dual frame surveys are useful when no single frame with adequate coverage exists. However estimators from dual frame designs require knowledge of the frame memberships of each sampled unit. When this information is not available from the frame itself, it is often collected from the respondent. When respondents provide incorrect membership information, the resulting estimators of means or totals can be biased. A method for reducing this bias, using accurate membership information obtained about a subsample of respondents, is proposed. The properties of the new estimator are examined and compared to alternative estimators. The proposed estimator is applied to the data from the motivating example, which was a recreational angler survey, using an address frame and an incomplete fishing license frame.
机译:当存在具有足够覆盖范围的单个帧时,双帧调查非常有用。然而,来自双帧设计的估计器需要了解每个采样单元的帧成员资格。当框架本身无法从框架本身获得此信息时,通常会从受访者收集。当受访者提供不正确的会员信息时,所产生的手段或总计的估算可以偏见。提出了一种利用关于受访者的附带的准确成员资格信息来减少这种偏差的方法。检查新估计器的属性并与替代估算者进行比较。所提出的估计器应用于来自动机示例的数据,该示例是使用地址帧和不完整的捕捞许可证框架的娱乐垂钓调查。

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