Best subsets variable selection in nonnormal regression models
Best subsets variable selection in nonnormal regression models
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DOI：
10.1177/1536867x1501500406
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发表时间：
2015-01-01
期刊：
影响因子：
4.8
通讯作者：
Sheather, Simon
中科院分区：
文献类型：
--
作者：
Lindsey, Charles;Sheather, Simon
我们提出了一个新的程序，gvselect，它可以帮助用户在回归中执行变量选择。执行最佳子集变量选择，并为用户提供每个模型复杂性级别的最佳预测器组合。跳跃式（Furnival和Wilson， 1974, technometics 16: 499-511）算法使用候选模型的对数似然来应用。这允许用户在各种各样的正态和非正态回归模型上执行变量选择。我们的方法被描述在Lawless和Singhal （1978, biometics 34: 318-327）。
We present a new program, gvselect, that helps users perform variable selection in regression. Best subsets variable selection is performed and provides the user with the best combinations of predictors for each level of model complexity. The leaps-and-bounds (Furnival and Wilson, 1974, Technometrics 16: 499-511) algorithm is applied using the log likelihoods of candidate models. This allows the user to perform variable selection on a wide variety of normal and non-normal regression models. Our method is described in Lawless and Singhal (1978, Biometrics 34: 318-327).