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Inference on the marginal distribution of clustered data with informative cluster size

机译:用信息量大的群集推断群集数据的边际分布

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In spite of recent contributions to the literature, informative cluster size settings are not well known and understood. In this paper, we give a formal definition of the problem and describe it from different viewpoints. Data generating mechanisms, parametric and nonparametric models are considered in light of examples. Our emphasis is on nonparametric and robust approaches to the inference on the marginal distribution. Descriptive statistics and parameters of interest are defined as functionals and they are accompanied with a generally applicable testing procedure. The theory is illustrated with an example on patients with incomplete spinal cord injuries.
机译:尽管最近对文献做出了贡献,但是信息群集的大小设置并不太为人所知和理解。在本文中,我们给出了问题的正式定义,并从不同的角度对其进行了描述。根据示例考虑数据生成机制,参数模型和非参数模型。我们的重点是对边际分布进行推断的非参数和鲁棒方法。感兴趣的描述性统计数据和参数定义为功能,并且随附有通常适用的测试程序。举例说明脊髓不完全损伤患者的理论。

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