A semiparametric method of boundary correction for kernel density estimation

T. Alberts and R. J. Karunamuni

Statistics & Probability Letters, 2003, vol. 61, issue 3, 287-298

Abstract: We propose a new estimator for boundary correction for kernel density estimation. Our method is based on local Bayes techniques of Hjort (Bayesian Statist. 5 (1996) 223). The resulting estimator is semiparametric type estimator: a weighted average of an initial guess and the ordinary reflection method estimator. The proposed estimator is seen to perform quite well compared to other existing well-known estimators for densities which have the shoulder condition at the endpoints.

Keywords: Kernel; density; estimation; Boundary; effects; Linear; Bayes; methods; Mean; squared; error (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (1)

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