General Asymptotic Confidence Bands Based on Kernel-type Function Estimators |
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| Authors: | Deheuvels Paul Mason David M |
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| Institution: | (1) L.S.T.A., Université, Pierre et Marie Curie, 7 avenue du Chateau, F-92340 Bourg-la-Reine, France.;(2) Department of Food and Resource Economics, University of Delaware, 206 Townsend Hall, Newark, DE, 19717, U.S.A. |
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| Abstract: | We establish uniform and non-uniform asymptotic simultaneous confidence bands for functionals of the distribution based on
kernel-type estimators, which include the Nadaraya-Watson kernel estimators of regression functions and the Akaike-Parzen-Rosenblatt
kernel density estimators. Our theorems, based upon functional limit laws derived by modern empirical process theory, allow
data-driven local bandwidths for these statistics.
This revised version was published online in June 2006 with corrections to the Cover Date. |
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| Keywords: | non-parametric estimation density estimation regression estimation kernel estimation empirical processes functional estimation weak laws laws of large numbers |
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