Variable selection for logistic regression using a prediction-focused information criterion
Variable selection for logistic regression using a prediction-focused information criterion
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DOI：
10.1111/j.1541-0420.2006.00567.x
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发表时间：
2006-12-01
期刊：
影响因子：
1.9
通讯作者：
Van Kerckhoven, Johan
中科院分区：
文献类型：
--
作者：
Claeskens, Gerda;Croux, Christophe;Van Kerckhoven, Johan
在生物统计实践中，通常使用信息标准作为模型选择的指南。我们提出了新版本的聚焦信息准则（FIC）的变量选择逻辑回归。根据需要估计的数量，FIC可能提供不同的选定变量集。FIC的标准版本测量所选模型中感兴趣数量的估计量的均方误差。在本文中，我们提出了更通用的FIC版本，允许其他风险度量，例如基于LP错误的风险度量。当事件的预测很重要时，就像在医疗应用中经常发生的那样，我们使用错误率作为自然风险度量来构建FIC。通过模拟研究和应用于糖尿病视网膜病变的研究，说明了使用的信息标准，这取决于感兴趣的数量和选定的风险措施的优点。
In biostatistical practice, it is common to use information criteria as a guide for model selection. We propose new versions of the focused information criterion (FIC) for variable selection in logistic regression. The FIC gives, depending on the quantity to be estimated, possibly different sets of selected variables. The standard version of the FIC measures the mean squared error of the estimator of the quantity of interest in the selected model. In this article, we propose more general versions of the FIC, allowing other risk measures such as the one based on LP error. When prediction of an event is important, as is often the case in medical applications, we construct an FIC using the error rate as a natural risk measure. The advantages of using an information criterion which depends on both the quantity of interest and the selected risk measure are illustrated by means of a simulation study and application to a study on diabetic retinopathy.