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Testing of Coarsening Mechanisms: Coarsening at Random Versus Subgroup Independence

机译:粗化机制的测试:随机粗化与子组独立性

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Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarsening at random (CAR) to force them to be single-valued. Using the PASS data as an example, we re-illustrate the impossibility to test CAR and contrast it to another type of uninformative coarsening called subgroup independence (SI). It turns out that SI is testable here.
机译:由于粗(NED)数据自然地诱导设定值估计器,分析师通常假设随机(汽车)粗略,以迫使它们单值。使用传递数据作为示例,我们重新说明了测试汽车的不可能,并将其对比,以众多类型的无信息粗化(Si)。事实证明,SI在这里是可测试的。

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