On subset selection in non-parametric stochastic regression
On subset selection in non-parametric stochastic regression
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发表时间：
1994
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通讯作者：
Q. Yao;H. Tong
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作者：
Q. Yao;H. Tong
本文关注的是在非线性随机回归框架内使用基于条件均值核估计的交叉验证方法来选择随机回归量的子集。在观测值严格平稳且绝对规则的假设下，我们表明交叉验证选择是一致的。进一步证明了所选模型的两种渐近效率。模拟数据和真实数据都用作说明。
This paper is concerned with the use of a cross-validation method based on the kernel estimate of the conditional mean for the subset selection of stochastic regressors within the framework of non-linear stochastic regression. Under the assumption that the observations are strictly stationary and absolutely regular, we show that the cross-validatory selection is consistent. Furthermore, two kinds of asymptotic efficiency of the selected model are proved. Both simulated and real data are used as illustrations.