Semiparametric quantile estimation for varying coefficient partially linear measurement errors models
Semiparametric quantile estimation for varying coefficient partially linear measurement errors models
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DOI：
10.1214/17-bjps357
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发表时间：
2018-08
影响因子：
1
通讯作者：
Jun Zhang;Yan Zhou;Xia Cui;Wang-li Xu
中科院分区：
文献类型：
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作者：
Jun Zhang;Yan Zhou;Xia Cui;Wang-li Xu
研究了变系数部分线性模型中某些线性协变量易出错，但其辅助变量可用的情形。在校正了易错协变量后，我们研究了参数系数和非参数变系数函数的分位数回归估计，并发展了一个半参数复合分位数估计方法。建立了估计量的渐近性质，并在适当的带宽条件下达到最佳收敛速度。仿真研究进行了评估所提出的方法的性能，并分析了一个真实的数据集作为一个例子。
We study varying coefficient partially linear models when some linear covariates are error-prone, but their ancillary variables are available. After calibrating the error-prone covariates, we study quantile regression estimates for parametric coefficients and nonparametric varying coefficient functions, and we develop a semiparametric composite quantile estimation procedure. Asymptotic properties of the proposed estimators are established, and the estimators achieve their best convergence rate with proper bandwidth conditions. Simulation studies are conducted to evaluate the performance of the proposed method, and a real data set is analyzed as an illustration.