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Two-step estimation of time-varying additive model for locally stationary time series
Hu, Lixia1; Huang, Tao2; You, Jinhong2
刊名COMPUTATIONAL STATISTICS & DATA ANALYSIS
2019-02
卷号130页码:94-110
关键词Time-varying additive model Locally stationary process alpha-mixing Local linear estimator Tensor product
ISSN号0167-9473
DOI10.1016/j.csda.2018.08.023
英文摘要In the analysis of locally stationary process, a time-varying additive model (tvAM) can effectively capture the dynamic feature of regression function. In combination with the strengths of tensor product of B-spline smoothing and local linear smoothing method, a two-step estimation method is proposed. It is shown that the proposed estimator is uniformly consistent and asymptotically oracle efficient as if the other component functions were known. Furthermore, a nonparametric bootstrap procedure is proposed to test the time-varying property of regression function. Simulation studies investigate the finite-sample performance of the proposed methods and validate the asymptotic theory. An environmental dataset illustrating the proposed method is also considered. (C) 2018 Elsevier B.V. All rights reserved.
WOS研究方向Computer Science ; Mathematics
语种英语
出版者ELSEVIER SCIENCE BV
WOS记录号WOS:000447571500006
内容类型期刊论文
源URL[http://10.2.47.112/handle/2XS4QKH4/367]  
专题上海财经大学
通讯作者Huang, Tao
作者单位1.Shanghai Lixin Univ Accounting & Finance, Sch Stat & Math, Shanghai, Peoples R China;
2.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China
推荐引用方式
GB/T 7714
Hu, Lixia,Huang, Tao,You, Jinhong. Two-step estimation of time-varying additive model for locally stationary time series[J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS,2019,130:94-110.
APA Hu, Lixia,Huang, Tao,&You, Jinhong.(2019).Two-step estimation of time-varying additive model for locally stationary time series.COMPUTATIONAL STATISTICS & DATA ANALYSIS,130,94-110.
MLA Hu, Lixia,et al."Two-step estimation of time-varying additive model for locally stationary time series".COMPUTATIONAL STATISTICS & DATA ANALYSIS 130(2019):94-110.
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