Statistical Inference on Seemingly Unrelated Single-Index Regression Models

doi:10.1007/s10255-016-0615-4

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	Statistical Inference on Seemingly Unrelated Single-Index Regression Models
	He, Bing 1; You, Jin-hong 2; Chen, Min3
	2016-10-01
发表期刊	ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES
ISSN	0168-9673
卷号	32 期号:4 页码:945-956
摘要	In this article, we consider a class of seemingly unrelated single-index regression models. By taking the contemporaneous correlation among equations into account we construct the weighted estimators (WEs) for unknown parameters of the coefficients and the improved local polynomial estimators for the unknown functions, respectively. We establish the asymptotic normalities of these estimators, and show both of them are more asymptotically efficient than those ignoring the contemporaneous correlation. The performances of the proposed procedures are evaluated through simulation studies.
关键词	seemingly unrelated contemporaneous correlation single-index weighted estimation
DOI	10.1007/s10255-016-0615-4
语种	英语
资助项目	National Natural Science Foundation of China[11471140]
WOS研究方向	Mathematics
WOS类目	Mathematics, Applied
WOS记录号	WOS:000387481600013
出版者	SPRINGER HEIDELBERG
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/24133
专题	应用数学研究所
通讯作者	He, Bing
作者单位	1.Jilin Univ, Sch Math, Changchun 130012, Peoples R China 2.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	He, Bing,You, Jin-hong,Chen, Min. Statistical Inference on Seemingly Unrelated Single-Index Regression Models[J]. ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES,2016,32(4):945-956.
APA	He, Bing,You, Jin-hong,&Chen, Min.(2016).Statistical Inference on Seemingly Unrelated Single-Index Regression Models.ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES,32(4),945-956.
MLA	He, Bing,et al."Statistical Inference on Seemingly Unrelated Single-Index Regression Models".ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES 32.4(2016):945-956.