Adaptive penalized splines for data smoothing

被引：14

作者：

Yang, Lianqiang ^{[1
,2
]}

Hong, Yongmiao ^{[2
]}

机构：

[1] Anhui Univ, Sch Math Sci, Hefei 230601, Peoples R China

[2] Cornell Univ, Dept Econ, Ithaca, NY 14853 USA

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2017年 / 108卷

关键词：

Nonparametric regression; Data smoothing; Penalized splines; Adaptivity; Local penalty; BAYESIAN P-SPLINES; REGRESSION SPLINES; SELECTION; PENALTY;

D O I：

10.1016/j.csda.2016.10.022

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Data driven adaptive penalized splines are considered via the principle of constrained regression. A locally penalized vector based on the local ranges of the data is generated and added into the penalty matrix of the classical penalized splines, which remarkably improves the local adaptivity of the model for data heterogeneity. The algorithm complexity and simulations are studied. The results show that the adaptive penalized splines outperform the smoothing splines, l(1) trend filtering and classical penalized splines in estimating functions with inhomogeneous smoothness. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：70 / 83

页数：14

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