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首页> 外文期刊>Statistics in medicine >Selecting variables in non-parametric regression models for binary response. An application to the computerized detection of breast cancer.
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Selecting variables in non-parametric regression models for binary response. An application to the computerized detection of breast cancer.

机译：在非参数回归模型中为二进制响应选择变量。在乳腺癌计算机检测中的应用。

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摘要

In many biomedical applications, interest lies in being able to distinguish between two possible states of a given response variable, depending on the values of certain continuous predictors. If the number of predictors, p, is high, or if there is redundancy among them, it then becomes important to decide on the selection of the best subset of predictors that will be able to obtain the models with greatest discrimination capacity. With this aim in mind, logistic generalized additive models were considered and receiver operating characteristic (ROC) curves were applied in order to determine and compare the discriminatory capacity of such models. This study sought to develop bootstrap-based tests that allow for the following to be ascertained: (a) the optimal number q
机译：在许多生物医学应用中，人们的兴趣在于能够根据某些连续预测变量的值来区分给定响应变量的两种可能状态。如果预测变量的数量p高，或者它们之间存在冗余，那么决定选择最佳预测变量子集就变得很重要，该子集将能够获得具有最大判别能力的模型。出于这个目的，考虑了逻辑广义加性模型，并应用了接收器工作特性（ROC）曲线以确定和比较这种模型的区分能力。这项研究试图开发基于引导程序的测试，以便确定以下内容：（a）预测变量的最佳数量q

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