The correction for attenuation due to measurement error: Clarifying concepts and creating confidence sets
The correction for attenuation due to measurement error: Clarifying concepts and creating confidence sets
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DOI：
10.1037/1082-989x.10.2.206
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发表时间：
2005-06-01
影响因子：
7
通讯作者：
Charles, EP
中科院分区：
文献类型：
--
作者：
Charles, EP
测量误差引起的衰减校正 (CAME) 受到了许多历史批评，其中大部分可以追溯到 CAME 推理使用能力有限。总结了过去确定 CAME 置信区间的尝试，并讨论了它们的局限性。作者认为，推理需要置信集来划分那些可能产生所获得值的总体参数，而不是指示可能由给定总体产生的样本，并且大多数研究人员倾向于混淆这两种类型的置信集。提出了三种不同的蒙特卡罗方法，每种方法都提供了在新概念下检查置信集的不同方法。探索这些方法对 CAME 的影响表明对其他统计数据的潜在影响。
The correction for attenuation due to measurement error (CAME) has received many historical criticisms, most of which can be traced to the limited ability to use CAME inferentially. Past attempts to determine confidence intervals for CAME are summarized and their limitations discussed. The author suggests that inference requires confidence sets that demarcate those population parameters likely to have produced an obtained value-rather than indicating the samples likely to be produced by a given population-and that most researchers tend to confuse these 2 types of confidence sets. Three different Monte-Carlo methods are presented, each offering a different way of examining confidence sets under the new conceptualization. Exploring the implications of these approaches for CAME suggests potential consequences for other statistics.