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《Science of Surveying and Mapping》 2011-01
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Parameter estimation on random regular matrix in regression model error compensation semi parametric method

ZHANG Zhi-wei,HU Wu-sheng,HUANG Xiao-ming(Traffic Institute of Southeast University,Nanjing 210096,China)  
The regression fitting model established with the experiment observation data inevitably has the model error.The model error as a non-parametric component included the semi parametric model of regression model is a significant regression model.The use of penalized least squares principle was taken into consideration of the predicted point to derive the semi parametric regression model parameter and the model error's estimator in the paper,and the precision evaluation was carried out to the estimator to give the estimator and the precision evaluation corresponding formula.In the basis of theoretical analysis,it obtained the conclusion that the parameter and the model error still had unique solution so long as the regular matrix R selection causes the normal equation coefficient matrix to be reversible and rank(RA)=t.The correctness and the strong stability of the conclusion were confirmed through several examples and the model effect was better.The conclusion provided a referencial way of thinking for the choice of regular matrix in semi parametric regression model parameter estimation.
【Fund】: 国家863计划项目“空间数据挖掘的神经网络技术研究”(2007AA12Z228)
【CateGory Index】: P207
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