Polynomial spline estimation for partial functional linear regression models

被引：0
作者：
Jianjun Zhou
Zhao Chen
Qingyan Peng
机构：
[1] Yunnan University,School of Mathematics and Statistics
[2] The Pennsylvania State University,Department of Statistics
来源：
Computational Statistics | 2016年 / 31卷
关键词：
Functional data analysis; Polynomial spline; Asymptotic normality; Rates of convergence;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Because of its orthogonality, interpretability and best representation, functional principal component analysis approach has been extensively used to estimate the slope function in the functional linear model. However, as a very popular smooth technique in nonparametric/semiparametric regression, polynomial spline method has received little attention in the functional data case. In this paper, we propose the polynomial spline method to estimate a partial functional linear model. Some asymptotic results are established, including asymptotic normality for the parameter vector and the global rate of convergence for the slope function. Finally, we evaluate the performance of our estimation method by some simulation studies.
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页码：1107 / 1129
页数：22
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