Consistent covariate selection and post model selection inference in semiparametric regression
Consistent covariate selection and post model selection inference in semiparametric regression
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DOI：
10.1214/009053604000000247
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发表时间：
2004-06-01
影响因子：
4.5
通讯作者：
Bunea, F
中科院分区：
文献类型：
--
作者：
Bunea, F
本文提出了Y-i = β 'X-i + f (T-i) + W-i, i = 1，…半参数回归模型的估计模型选择技术。参数和非参数分量通过这个过程同时估计。估计是基于有限维模型的集合，使用惩罚最小二乘标准进行选择。我们表明，通过剪裁为非参数回归开发的惩罚项到半参数模型，我们可以一致地估计线性部分的非零系数子集。此外，所选的线性分量的估计量是渐近正态的。
This paper presents a model selection technique of estimation in semiparametric regression models of the type Y-i = beta'X-i + f (T-i) + W-i, i = 1,..., n. The parametric and nonparametric components are estimated simultaneously by this procedure. Estimation is based on a collection of finite-dimensional models, using a penalized least squares criterion for selection. We show that by tailoring the penalty terms developed for nonparametric regression to semiparametric models, we can consistently estimate the subset of nonzero coefficients of the linear part. Moreover, the selected estimator of the linear component is asymptotically normal.