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Difference based estimation for partially linear regression models with measurement errors
Zhao, Haibing; You, Jinhong
刊名JOURNAL OF MULTIVARIATE ANALYSIS
2011-11
卷号102期号:10页码:1321-1338
关键词Difference based estimation Partially linear model Measurement error
ISSN号0047-259X
DOI10.1016/j.jmva.2011.04.009
英文摘要This paper is concerned with the estimating problem of the partially linear regression models where the linear covariates are measured with additive errors. A difference based estimation is proposed to estimate the parametric component. We show that the resulting estimator is asymptotically unbiased and achieves the semiparametric efficiency bound if the order of the difference tends to infinity. The asymptotic normality of the resulting estimator is established as well. Compared with the corrected profile least squares estimation, the proposed procedure avoids the bandwidth selection. In addition, the difference based estimation of the error variance is also considered. For the nonparametric component, the local polynomial technique is implemented. The finite sample properties of the developed methodology is investigated through simulation studies. An example of application is also illustrated. (C) 2011 Elsevier Inc. All rights reserved.
WOS研究方向Mathematics
语种英语
出版者ELSEVIER INC
WOS记录号WOS:000293048300001
内容类型期刊论文
源URL[http://10.2.47.112/handle/2XS4QKH4/2229]  
专题上海财经大学
通讯作者You, Jinhong
作者单位Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China
推荐引用方式
GB/T 7714
Zhao, Haibing,You, Jinhong. Difference based estimation for partially linear regression models with measurement errors[J]. JOURNAL OF MULTIVARIATE ANALYSIS,2011,102(10):1321-1338.
APA Zhao, Haibing,&You, Jinhong.(2011).Difference based estimation for partially linear regression models with measurement errors.JOURNAL OF MULTIVARIATE ANALYSIS,102(10),1321-1338.
MLA Zhao, Haibing,et al."Difference based estimation for partially linear regression models with measurement errors".JOURNAL OF MULTIVARIATE ANALYSIS 102.10(2011):1321-1338.
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