A goodness-of-fit test for logistic regression models based on case-control data
A goodness-of-fit test for logistic regression models based on case-control data
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DOI：
10.1093/biomet/84.3.609
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发表时间：
1997-09-01
期刊：
影响因子：
2.7
通讯作者：
Zhang, B
中科院分区：
文献类型：
--
作者：
Qin, J;Zhang, B
我们在病例对照抽样计划下检验逻辑回归假设。重新参数化后，假设的逻辑回归模型相当于两个样本半参数模型，其中两个密度函数的对数比在数据中是线性的。通过将此模型与有偏抽样模型相鉴别，我们提出了一个Kolmogorov-Smirnov型统计量来检验Logistic链接函数的有效性。此外，我们指出该检验统计量也可以用于混合抽样。我们提出了一个引导程序沿着与模拟和分析两个真实的数据集的一些结果。
We test the logistic regression assumption under a case-control sampling plan. After reparameterisation, the assumed logistic regression model is equivalent to a two-sample semiparametric model in which the log ratio of two density functions is linear in data. By identifying this model with a biased sampling model, we propose a Kolmogorov-Smirnov-type statistic to test the validity of the logistic link function. Moreover, we point out that this test statistic can also be used in mixture sampling. We present a bootstrap procedure along with some results on simulation and on analysis of two real datasets.