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Root-n convergent transformation-kernel density estimation 

Author: Lijian Yang a
Affiliation:   a Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, USA
DOI: 10.1080/10485250008832818
Publication Frequency: 8 issues per year
Published in: journal Journal of Nonparametric Statistics, Volume 12, Issue 4 2000 , pages 447 - 474
Formats available: PDF (English)
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Abstract

Transformation from a parametrized family can be combined with kernel density estimation for improved effectiveness. Pilot estimators had been proposed for the parameter that gives the optimal transformation, yet their rates of convergence had not been resolved. In this paper, the rates of convergence are given. An improved estimator is also proposed which achieves the desirable root-n rate of convergence.
Keywords: Global bandwidth; improved estimator; optimal parameter; pilot bandwidth; vector parameter
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