nonparametricidentificationfornonlinearautoregressivetimeseriesmodelsconvergencerates

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	nonparametricidentificationfornonlinearautoregressivetimeseriesmodelsconvergencerates
	Cheng Ping; Lu Zudi
	1999
发表期刊	chineseannalsofmathematicsseriesb
ISSN	0252-9599
卷号	20 期号:2 页码:173
摘要	In 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.
语种	英语
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/47252
专题	中国科学院数学与系统科学研究院
作者单位	中国科学院数学与系统科学研究院
推荐引用方式 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.