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Double smoothing local linear estimation in nonlinear time series

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成果类型:
期刊论文
作者:
Prasangika, K. D.;Tang, Wan;Yao, Zeng;Zuo, Guoxin
通讯作者:
Zuo, Guoxin(zuogx@mail.ccnu.edu.cn)
作者机构:
[Prasangika, K. D.] Univ Ruhuna, Dept Math, Matara, Sri Lanka.
[Tang, Wan] Tulane Univ, Dept Biostat & Data Sci, New Orleans, LA 70118 USA.
[Yao, Zeng; Zuo, Guoxin] Cent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R China.
通讯机构:
[Guoxin Zuo] S
School of Mathematics and Statistics, Central China Normal University, Wuhan, P.R. China
语种:
英文
关键词:
Non parametric regression;local linear regression;double smoothing local linear regression;time series;asymptotic properties
期刊:
Communications in Statistics - Theory and Methods
ISSN:
0361-0926
年:
2023
卷:
52
期:
5
页码:
1385-1399
基金类别:
This work was supported by the National Natural Science Foundation of China (No.11471252) and Fundamental Research Funds for Central Universities (No. CCNU18ZDPY08).
机构署名:
本校为其他机构
院系归属:
数学与统计学学院
摘要:
We generalize the double smoothing local linear regression method to nonparametric regression of time series. Under a strong mixing condition for the dependence of the time series, we show that after another round of smoothing based on the local linear regression estimates, the double smoothing local linear estimate will have reduced asymptotic bias, while keeping the variance at the same asymptotic order. The asymptotic bias reduces from the order of h 2 for the local linear estimates to h 4 for the double smoothing local linear estimates, whe...

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