On the efficacy of the rank transformation in stepwise logistic and discriminant analysis.
On the efficacy of the rank transformation in stepwise logistic and discriminant analysis.
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关于逐步逻辑和判别分析中等级变换的有效性。
DOI：
10.1002/sim.4780120206
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发表时间：
1993
影响因子：
2
通讯作者：
Woolson,RF
中科院分区：
文献类型：
--
作者：
O'Gorman,TW;Woolson,RF
我们已经评估了四个逐步变量选择过程中常用的医学和流行病学研究的性能。这四个程序是判别和逻辑回归及其秩变换版本，其中自变量被其秩取代。我们通过计算机生成了两组数据，这些数据来自具有各种样本大小和协方差矩阵的几个分布。这两个排序程序都增加了正确选择与对数正态分布或污染分布生成的数据的组成员关系相关的变量的机会。对于正态分布的数据，排序程序对变量选择的影响很小。当样本量超过100时，秩转换判别分析和秩转换logistic回归在选择变量方面同样有效。秩变换判别分析是上级为较小的数据集。我们讨论了临床和流行病学研究这项研究的结果的影响。
We have evaluated the performance of four stepwise variable selection procedures commonly used in medical and epidemiologic research. The four procedures are discriminant and logistic regression and their rank transformed versions, where the independent variables are replaced by their ranks. We generated, by computer, data for two groups from several distributions with a variety of sample sizes and covariance matrices. The two ranking procedures each increased the chance of correctly selecting those variables related to group membership for data generated from log‐normal or contaminated distributions. For normally distributed data the ranking procedure had little effect on variable selection. Rank transformed discriminant analysis and rank transformed logistic regression were equally effective in selecting variables when sample sizes exceeded 100. Rank transformed discriminant analysis was superior for smaller data sets. We discuss the implications of the results of this study for clinical and epidemiologic research.