EFFICIENT ESTIMATION OF SEEMINGLY UNRELATED ADDITIVE NONPARAMETRIC REGRESSION MODELS  

作　　者：YUAN Yuan YOU Jinhong ZHOU Yong 

机构地区：[1]School of Statistics and Management,Shanghai University of Finance and Economics [2]Academy of Mathematics and Systems Science,Chinese Academy of Sciences

出　　处：《Journal of Systems Science & Complexity》2013年第4期595-608,共14页系统科学与复杂性学报（英文版）

基　　金：supported by National Natural Science Funds for Distinguished Young Scholar under Grant No.70825004;National Natural Science Foundation of China under Grant Nos.10731010 and 10628104;the National Basic Research Program under Grant No.2007CB814902;Creative Research Groups of China under Grant No.10721101;supported by leading Academic Discipline Program,211 Project for Shanghai University of Finance and Economics(the 3rd phase)and project number:B803;supported by grants from the National Natural Science Foundation of China under Grant No.11071154

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 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：Additive structure asymptotic normality nonparametric modelling polynomial spline seemingly unrelated regression two-stage estimation. 

分 类 号：C81[社会学—统计学]