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 c...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.展开更多
基金supported by National Natural Science Funds for Distinguished Young Scholar under Grant No.70825004National Natural Science Foundation of China under Grant Nos.10731010 and 10628104+3 种基金the National Basic Research Program under Grant No.2007CB814902Creative Research Groups of China under Grant No.10721101supported by leading Academic Discipline Program,211 Project for Shanghai University of Finance and Economics(the 3rd phase)and project number:B803supported by grants from the National Natural Science Foundation of China under Grant No.11071154
文摘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.
