In this article, we consider a class of kernel quantile estimators which is the linear combi- nation of order statistics. This class of kernel quantile estimators can be regarded as an extension of some existing estim...In this article, we consider a class of kernel quantile estimators which is the linear combi- nation of order statistics. This class of kernel quantile estimators can be regarded as an extension of some existing estimators. The exact mean square error expression for this class of estimators will be provided when data are uniformly distributed. The implementation of these estimators depends mostly on the bandwidth selection. We then develop an adaptive method for bandwidth selection based on the intersection confidence intervals （ICI） principle. Monte Carlo studies demonstrate that our proposed approach is comparatively remarkable. We illustrate our method with a real data set.展开更多
基金Supported by Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China(Grant Nos.10XNL018,10XNK025)National Natural Science Foundation of China(Grant No.11271368)+3 种基金Beijing Planning Office of Philosophy and Social Science(Grant No.12JGB051)China Statistical Research Project(Grant No.2011LZ031)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(No.13AZD064)
文摘In this article, we consider a class of kernel quantile estimators which is the linear combi- nation of order statistics. This class of kernel quantile estimators can be regarded as an extension of some existing estimators. The exact mean square error expression for this class of estimators will be provided when data are uniformly distributed. The implementation of these estimators depends mostly on the bandwidth selection. We then develop an adaptive method for bandwidth selection based on the intersection confidence intervals （ICI） principle. Monte Carlo studies demonstrate that our proposed approach is comparatively remarkable. We illustrate our method with a real data set.
