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Examining Measure Correlations With Incomplete Data Sets

机译:使用不完整的数据集检查量度相关性

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摘要

A 2-stage procedure for estimation and testing of observed measure correlations in the presence of missing data is discussed. The approach uses maximum likelihood for estimation and the false discovery rate concept for correlation testing. The method can be used in initial exploration-oriented empirical studies with missing data, where it is of interest to estimate manifest variable interrelationship indexes and test hypotheses about their population values. The procedure is applicable also with violations of the underlying missing at random assumption, via inclusion of auxiliary variables. The outlined approach is illustrated with data from an aging research study.
机译:讨论了在缺少数据的情况下估计和测试观察到的度量相关性的两阶段过程。该方法将最大似然用于估计,将错误发现率概念用于相关性测试。该方法可用于缺少数据的初始以勘探为导向的实证研究,在此方面,估计明显的变量相互关系指数并检验有关其总体价值的假设很有意义。通过包含辅助变量,该过程也适用于违反随机假设的基本缺失的情况。使用老化研究的数据说明了概述的方法。

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