Seemingly Unrelated Nonparametric Model with the Measurement Error Data Problem

摘要：

In this paper, by the deconvolution kernel density estimator their simple local regression version of the seemingly unrelated nonparametric regression model can be extended to the measurement error data where variance of disturbance in an equation is larger than that in the preceding one and all of the correlation coefficients between the disturbances across the equations are positive. The resulting convergence rates of the estimators are obtained and are shown to achieve the optimal rates of convergence.

作者: WANG Litong

作者单位: Department of Applied Mathematics, Zhejiang University of Technology, P.R.China, 310023

会议类型: 国际会议

会议名称: The Fourth International Institute of Statistics & Management Engineering Symposium(第四届(2011)国际统计与管理工程研讨会 IISMES 2011)

会议地点: 大连

会议语种:英文

页码: 1420-1423

在线出版日期: 2011-07-24（万方平台首次上网日期，不代表论文的发表时间）