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Nonparametric identification for nonlinear autoregressive time series models: Convergence rates
Lu, ZD; Cheng, P
1999-04-01
Source PublicationCHINESE ANNALS OF MATHEMATICS SERIES B
ISSN0252-9599
Volume20Issue:2Pages:173-184
AbstractIn this paper, the optimal convergence rates of estimators based on kernel approach for nonlinear AR model are investigated in the sense of Stone([17, 18]). By combining the alpha-mixing property of the stationary solution with the characteristics of the model itself, the restrictive conditions in the literature which are not easy to be satisfied by the nonlinear AR model are removed, and the mild conditions are obtained to guarantee the optimal rates of the estimator of autoregression function. In addition, the strongly consistent estimator of the variance of white noise is also constructed.
Keywordnonlinear AR model optimal convergence rates Kernel approach autoregression function variance of white noise consistency
Language英语
WOS Research AreaMathematics
WOS SubjectMathematics
WOS IDWOS:000080071600005
PublisherBALTZER SCI PUBL BV
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/14379
Collection中国科学院数学与系统科学研究院
AffiliationAcad Sinica, Inst Syst Sci, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Lu, ZD,Cheng, P. Nonparametric identification for nonlinear autoregressive time series models: Convergence rates[J]. CHINESE ANNALS OF MATHEMATICS SERIES B,1999,20(2):173-184.
APA Lu, ZD,&Cheng, P.(1999).Nonparametric identification for nonlinear autoregressive time series models: Convergence rates.CHINESE ANNALS OF MATHEMATICS SERIES B,20(2),173-184.
MLA Lu, ZD,et al."Nonparametric identification for nonlinear autoregressive time series models: Convergence rates".CHINESE ANNALS OF MATHEMATICS SERIES B 20.2(1999):173-184.
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