Efficient estimation of seemingly unrelated additive nonparametric regression models

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	Efficient estimation of seemingly unrelated additive nonparametric regression models
其他题名	EFFICIENT ESTIMATION OF SEEMINGLY UNRELATED ADDITIVE NONPARAMETRIC REGRESSION MODELS
	Yuan Yuan 1; You Jinhong 1; Zhou Yong 1
	2013-01-01
发表期刊	JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
ISSN	1009-6124
卷号	26 期号:4 页码:595-608
摘要	This paper is concerned with the estimating problem of seemingly unrelated (SU) nonparametric additive regression models. A polynomial spline based two-stage efficient approach is proposed to estimate the nonparametric components, which takes both of the additive structure and correlation between equations into account. The asymptotic normality of the derived estimators are establishedi. The authors also show they own some advantages, including they are asymptotically more efficient than those based on only the individual regression equation and have an oracle property, which is the asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty. Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure. Applying the proposed procedure to a real data set is also made.
其他摘要	This paper is concerned with the estimating problem of seemingly unrelated (SU) nonpara-metric additive regression models. A polynomial spline based two-stage efficient approach is proposed to estimate the nonparametric components, which takes both of the additive structure and correlation between equations into account. The asymptotic normality of the derived estimators are established. The authors also show they own some advantages, including they are asymptotically more efficient than those based on only the individual regression equation and have an oracle property, which is the asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty. Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure. Applying the proposed procedure to a real data set is also made.
关键词	BAYESIAN-INFERENCE CONSISTENCY Additive structure asymptotic normality nonparametric modelling polynomial spline seemingly unrelated regression two-stage estimation
收录类别	CSCD
语种	英语
资助项目	[National Natural Science Funds for Distinguished Young Scholar] ; [National Natural Science Foundation of China] ; [National Basic Research Program] ; [Creative Research Groups of China] ; [leading Academic Discipline Program, 211 Project for Shanghai University of Finance and Economics (the 3rd phase)]
CSCD记录号	CSCD:4908046
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/55065
专题	中国科学院数学与系统科学研究院
作者单位	1.上海大学 2.中国科学院数学与系统科学研究院
推荐引用方式 GB/T 7714	Yuan Yuan,You Jinhong,Zhou Yong. Efficient estimation of seemingly unrelated additive nonparametric regression models[J]. JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY,2013,26(4):595-608.
APA	Yuan Yuan,You Jinhong,&Zhou Yong.(2013).Efficient estimation of seemingly unrelated additive nonparametric regression models.JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY,26(4),595-608.
MLA	Yuan Yuan,et al."Efficient estimation of seemingly unrelated additive nonparametric regression models".JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY 26.4(2013):595-608.