Abstract
In this article, we consider a class of kernel quantile estimators which is the linear combination 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.
Similar content being viewed by others
References
Behnke, A., Wilmore, J.: Evaluation and Regulation of Body Build and Composition, Englewood Cliffs, N. J., Prentice Hall, 1974
Efromovich, S. Y., Pinsker, M. S.: Learning algorithm for nonparametric filtering. Automation and Remote Control, 45, 1434–1440 (1984)
Falk, M.: Relative deficiency of kernel type estimators of quantiles. Ann. Stat., 12, 261–268 (1984)
Falk, M.: Asymptotic normality of the kernel quantile estimator. Ann. Stat., 13, 428–433 (1985)
Goldenshluger, A., Nemirovski, A.: On spatial adaptive estimation of nonparametric regression. Math. Meth. Stat., 6, 135–170 (1997)
Katkovnik, V.: A new method for varying adaptive bandwidth selection. IEEE Trans. Signal Processing, 47, 2567–2571 (1999)
Katkovnik, V., Egiazarian, K., Astola, J.: Application of the ICI principle to window size adaptive median filtering. Signal Processing, 83, 251–257 (2003)
Lepskii, O. V.: Asymptotically minimax adaptive estimation. I. Upper bounds. Optimal adaptive estimates Teor. Veroyatnost. i Primenen., 36, 645–659 (1991)
Reiss, R. D.: Approximate Distributions of Order Statistics: with Applications to Nonparametric Statistics, Chapter 8, Springer, New York, 1989
Roger, W. J.: Fitting percentage of body fat to simple body measurements. J. Stat. Education, 4, n.1 (1996)
Sheather, S. J., Marron, J. S.: Kernel quantile estimators. J. Amer. Stat. Assoc., 85, 410–416 (1989)
Tian, M. Z.: Robust estimation in inverse problems via quantile coupling. Sci. China Math., 55, 1029–1041 (2012)
Tian, M. Z., Tang, M. L., Chan, P. S.: Semiparametric quantile modelling of hierarchical data. Acta Math. Sin., Engl. Ser., 25, 597–616 (2009)
Yang, S. S.: A smooth nonparametric estimation of a quantile function. J. Amer. Stat. Assoc., 80, 1004–1011 (1985)
Author information
Authors and Affiliations
Corresponding author
Additional information
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), Beijing Planning Office of Philosophy and Social Science (Grant No. 12JGB051), China Statistical Research Project (Grant No. 2011LZ031), and 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)
Rights and permissions
About this article
Cite this article
Fan, J.Y., Tang, M.L. & Tian, M.Z. Kernel quantile estimator with ICI adaptive bandwidth selection technique. Acta. Math. Sin.-English Ser. 30, 710–722 (2014). https://doi.org/10.1007/s10114-014-1233-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10114-014-1233-9