Spline-backfitted kernel smoothing of partially linear additive model
Spline-backfitted kernel smoothing of partially linear additive model
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DOI：
10.1016/j.jspi.2010.05.028
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发表时间：
2011-01-01
影响因子：
0.9
通讯作者：
Yang, Lijian
中科院分区：
文献类型：
--
作者：
Ma, Shujie;Yang, Lijian
针对部分线性加性模型，提出了一种基于样条库的核平滑方法。在平稳性和几何混合的假设下，所提出的函数和参数估计器在理论上是有效的，并且计算速度很快。这种优越的性质是通过连续地应用于数据样条平滑和核平滑来实现的。中变量和大变量的仿真实验都证实了渐近结果。对波士顿住房数据的应用是该方法的实际例证。(C)2010爱思唯尔B.V.保留所有权利。
A spline-backfitted kernel smoothing method is proposed for partially linear additive model. Under assumptions of stationarity and geometric mixing, the proposed function and parameter estimators are oracally efficient and fast to compute. Such superior properties are achieved by applying to the data spline smoothing and kernel smoothing consecutively. Simulation experiments with both moderate and large number of variables confirm the asymptotic results. Application to the Boston housing data serves as a practical illustration of the method. (c) 2010 Elsevier B.V. All rights reserved.