EFFICIENT ESTIMATION OF SEEMINGLY UNRELATED ADDITIVE NONPARAMETRIC REGRESSION MODELS

YUAN Yuan , YOU Jinhong , ZHOU Yong

Journal of Systems Science & Complexity ›› 2013, Vol. 26 ›› Issue (4) : 595-608.

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Journal of Systems Science & Complexity ›› 2013, Vol. 26 ›› Issue (4) : 595-608. DOI: 10.1007/s11424-012-8351-1
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EFFICIENT ESTIMATION OF SEEMINGLY UNRELATED ADDITIVE NONPARAMETRIC REGRESSION MODELS

  • YUAN Yuan1 , YOU Jinhong1 , ZHOU Yong2
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Abstract

This paper is concerned with the estimating problem of seemingly unrelated (SU) nonparametric additive regression models. A polynomial spline based two-stage ecient 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 ecient 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 nite sample performance of the proposed procedure. Applying the proposed procedure to a real data set is also made.

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YUAN Yuan , YOU Jinhong , ZHOU Yong. EFFICIENT ESTIMATION OF SEEMINGLY UNRELATED ADDITIVE NONPARAMETRIC REGRESSION MODELS. Journal of Systems Science and Complexity, 2013, 26(4): 595-608 https://doi.org/10.1007/s11424-012-8351-1

Funding

ZHOU's work was partially supported by National Natural Science Funds for Distinguished Young Scholar under Grant No. 70825004 and 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. Partially supported by leading Academic Discipline Program, 211 Project for Shanghai University of Finance and Economics (the 3rd phase) and project number: B803; YOU's research was supported by grants from the National Natural Science Foundation of China under Grant No. 11071154.

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