supported by the major research projects of Philosophy and Social Science of the Chinese Ministry of Education(Grant No.15JZD015);National Natural Science Foundation of China(Grant No.11271368);the major program of Beijing Philosophy and Social Science Foundation of China(Grant No.15ZDA17);project of Ministry of Education supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20130004110007);the Key Program of National Philosophy and Social Science Foundation Grant(Grant No.13AZD064);the major project of Humanities Social Science Foundation of Ministry of Education(Grant No.15JJD910001);Renmin University of China,the Special Developing and Guiding Fund for Building World-Class Universities(Disciplines)(Grant No.15XNL008);China Statistical Research Project(Grant No.2016LD03);the Fund of the Key Research Center of Humanities and Social Sciences in the general Colleges and Universities of Xinjiang Uygur Autonomous Region;General Research Fund of Hong Kong Special Administrative Region Research Grants Council General Research Fund(Grant Nos.14300514 and 14325612);Hong Kong Special Administrative Region-Research Grants Council Collaborative Research Fund(Grant No.City U8/CRG/12G);the Theme-Based Research Scheme of Hong Kong Special Administrative Region-Research Grants Council Theme Based Scheme(Grant No.T32-101/15-R)
An adaptive local smoothing method for nonpaxametric conditional quantile regression models is considered in this paper. Theoretical properties of the procedure are examined. The proposed method is fully adaptive in t...