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nonparametricidentificationfornonlinearautoregressivetimeseriesmodelsconvergencerates
Cheng Ping; Lu Zudi
1999
Source Publicationchineseannalsofmathematicsseriesb
ISSN0252-9599
Volume20Issue:2Pages:173
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.
Language英语
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/47252
Collection中国科学院数学与系统科学研究院
Affiliation中国科学院数学与系统科学研究院
Recommended Citation
GB/T 7714
Cheng Ping,Lu Zudi. nonparametricidentificationfornonlinearautoregressivetimeseriesmodelsconvergencerates[J]. chineseannalsofmathematicsseriesb,1999,20(2):173.
APA Cheng Ping,&Lu Zudi.(1999).nonparametricidentificationfornonlinearautoregressivetimeseriesmodelsconvergencerates.chineseannalsofmathematicsseriesb,20(2),173.
MLA Cheng Ping,et al."nonparametricidentificationfornonlinearautoregressivetimeseriesmodelsconvergencerates".chineseannalsofmathematicsseriesb 20.2(1999):173.
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