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An omnibus lack of fit test in logistic regression with sparse data

机译：具有稀疏数据的逻辑回归中的综合缺乏拟合检验

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摘要

The usefulness of logistic regression depends to a great extent on the correct specification of the relation between a binary response and characteristics of the unit on which the response is recoded. Currently used methods for testing for mis-specification (lack of fit) of a proposed logistic regression model do not perform well when a data set contains almost as many distinct covariate vectors as experimental units, a condition referred to as sparsity. A new algorithm for grouping sparse data to create pseudo replicates and using them to test for lack of fit is developed. A simulation study illustrates settings in which the new test is superior to existing ones. Analysis of a dataset consisting of the ages of menarche of Warsaw girls is also used to compare the new and existing lack of fit tests.
机译：Logistic回归的有用性在很大程度上取决于对二进制响应和响应所重新编码的单位特征之间关系的正确规定。当数据集包含与实验单位几乎一样多的不同协变量矢量时，当前使用的测试拟议的逻辑回归模型的错误指定（缺乏拟合）的方法效果不佳，这种情况称为稀疏性。开发了一种新算法，用于将稀疏数据分组以创建伪复制品，并使用它们来测试拟合是否不足。仿真研究说明了新测试优于现有测试的设置。对由华沙女孩初潮年龄组成的数据集进行的分析也用于比较新的和现有的缺乏适应性测试。

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