Nevalainen Jaakko, Datta Somnath, Oja Hannu
Department of Mathematics and Statistics, University of Turku, FI-20014 Turku, Finland.
University of Louisville, Louisville, USA.
Stat Pap (Berl). 2014 Feb 1;55(1):71-92. doi: 10.1007/s00362-013-0504-3.
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.
尽管近期有文献对此有所贡献，但信息性聚类大小设置并未得到广泛了解和认识。在本文中，我们给出了该问题的形式化定义，并从不同角度对其进行描述。结合示例考虑了数据生成机制、参数模型和非参数模型。我们重点关注对边际分布进行推断的非参数和稳健方法。描述性统计量和感兴趣的参数被定义为泛函，并伴有一个普遍适用的检验程序。通过一个关于不完全脊髓损伤患者的示例对该理论进行了说明。
