Variable selection for the single-index model
Variable selection for the single-index model
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DOI：
10.1093/biomet/asm008
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发表时间：
2007-03-01
期刊：
影响因子：
2.7
通讯作者：
Xia, Yingcun
中科院分区：
文献类型：
--
作者：
Kong, Efang;Xia, Yingcun
我们考虑单指标模型中的变量选择。我们证明了流行的leave-m-out交叉验证方法在单指标模型中具有与线性回归模型或非参数回归模型不同的行为。提出了一种新的一致性变量选择方法--分离交叉验证法。进一步的分析表明，该方法具有更好的有限样本性能，是计算上更容易比leave-m-out交叉验证。单独的交叉验证，适用于瑞士钞票数据和臭氧浓度数据，导致单指数模型与选定的变量，有更好的预测能力比模型的基础上所有的协变量。
We consider variable selection in the single-index model. We prove that the popular leave-m-out crossvalidation method has different behaviour in the single-index model from that in linear regression models or nonparametric regression models. A new consistent variable selection method, called separated crossvalidation, is proposed. Further analysis suggests that the method has better finite-sample performance and is computationally easier than leave-m-out crossvalidation. Separated crossvalidation, applied to the Swiss banknotes data and the ozone concentration data, leads to single-index models with selected variables that have better prediction capability than models based on all the covariates.