Penalized estimating functions and variable selection in semiparametric regression models
Penalized estimating functions and variable selection in semiparametric regression models
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DOI：
10.1198/016214508000000184
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发表时间：
2008-06-01
影响因子：
3.7
通讯作者：
Zeng, Donglin
中科院分区：
文献类型：
--
作者：
Johnson, Brent A.;Lin, D. Y.;Zeng, Donglin
提出了一种通过惩罚估计函数来选择半参数回归模型变量的一般策略。重要的应用包括具有删失响应的半参数线性回归和具有缺失预测因子的半参数回归。与已有的惩罚极大似然估计不同，本文提出的惩罚估计函数可以不涉及任何目标函数的导数，并且回归系数可以是离散的.我们建立了惩罚估计函数的一般渐近理论，并提出了合适的数值算法来实现所提出的估计。此外，我们开发了一种估计回归系数的渐近方差时，不能直接评估的方差估计的restling技术。仿真结果表明，所提出的方法在变量选择和方差估计方面具有良好的性能。我们使用Paul Coverdell卒中登记处的数据来说明我们的方法。
We propose a general strategy for variable selection in semiparametric regression models by penalizing appropriate estimating functions. Important applications include semiparametric linear regression with censored responses and semiparametric regression with missing predictors. Unlike the existing penalized maximum likelihood estimators, the proposed penalized estimating functions may not pertain to the derivatives of any objective functions and may be discrete in the regression coefficients. We establish a general asymptotic theory for penalized estimating functions and present suitable numerical algorithms to implement the proposed estimators. In addition, we develop a resampling technique to estimate the variances of the estimated regression coefficients when the asymptotic variances cannot be evaluated directly. Simulation studies demonstrate that the proposed methods perform well in variable selection and variance estimation. We illustrate our methods using data from the Paul Coverdell Stroke Registry.