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 |
DOI | 10.1016/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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