statisticalinferenceonseeminglyunrelatedsingleindexregressionmodels

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	statisticalinferenceonseeminglyunrelatedsingleindexregressionmodels
	He Bing 1; You Jinhong 2; Chen Min3
	2016
发表期刊	应用数学学报
ISSN	0168-9673
卷号	32 期号:4 页码:945
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.
语种	英语
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/46742
专题	应用数学研究所
作者单位	1.School of Mathematics,Jilin University 2.School of Statistics and Management,Shanghai University of Finance and Economics 3.中国科学院数学与系统科学研究院
推荐引用方式 GB/T 7714	He Bing,You Jinhong,Chen Min. statisticalinferenceonseeminglyunrelatedsingleindexregressionmodels[J]. 应用数学学报,2016,32(4):945.
APA	He Bing,You Jinhong,&Chen Min.(2016).statisticalinferenceonseeminglyunrelatedsingleindexregressionmodels.应用数学学报,32(4),945.
MLA	He Bing,et al."statisticalinferenceonseeminglyunrelatedsingleindexregressionmodels".应用数学学报 32.4(2016):945.