BACKWARD, FORWARD AND STEPWISE AUTOMATED SUBSET-SELECTION ALGORITHMS - FREQUENCY OF OBTAINING AUTHENTIC AND NOISE VARIABLES
BACKWARD, FORWARD AND STEPWISE AUTOMATED SUBSET-SELECTION ALGORITHMS - FREQUENCY OF OBTAINING AUTHENTIC AND NOISE VARIABLES
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DOI：
10.1111/j.2044-8317.1992.tb00992.x
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发表时间：
1992-11-01
影响因子：
2.6
通讯作者：
KESELMAN, HJ
中科院分区：
文献类型：
--
作者：
DERKSEN, S;KESELMAN, HJ
审查了自动子集搜索算法的使用，并讨论了有关模型选择和选择标准的问题。此外，还报道了一项蒙特卡洛研究，其中介绍了有关通过自动子集算法选择真实和噪声变量的频率的数据。特别地，研究了三种自动化子集算法的预测变量之间的相关性，候选预测变量的数量，样本的大小以及变量进入和删除的显着性水平：向后消除，向前选择，远期选择，选择和逐步。结果表明：（1）预测变量之间的相关程度影响了真实的预测变量进入最终模型的频率； （2）候选预测变量的数量影响了进入模型的噪声变量的数量； （3）样品的大小在确定最终模型中包含的真实变量的数量方面几乎没有实际重要性； （4）可以通过采用统计量来根据候选预测变量的总数而不是最终模型中变量数量来调整的统计量来忠实地估计人口多重确定系数。
The use of automated subset search algorithms is reviewed and issues concerning model selection and selection criteria are discussed. In addition, a Monte Carlo study is reported which presents data regarding the frequency with which authentic and noise variables are selected by automated subset algorithms. In particular, the effects of the correlation between predictor variables, the number of candidate predictor variables, the size of the sample, and the level of significance for entry and deletion of variables were studied for three automated subset algorithms: BACKWARD ELIMINATION, FORWARD SELECTION, and STEPWISE. Results indicated that: (1) the degree of correlation between the predictor variables affected the frequency with which authentic predictor variables found their way into the final model; (2) the number of candidate predictor variables affected the number of noise variables that gained entry to the model; (3) the size of the sample was of little practical importance in determining the number of authentic variables contained in the final model; and (4) the population multiple coefficient of determination could be faithfully estimated by adopting a statistic that is adjusted by the total number of candidate predictor variables rather than the number of variables in the final model.