A note on kernel density estimation for non-negative random variables

T. Sclocco () and M. Marzio ()
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T. Sclocco: “G. d'Annuzio” University
M. Marzio: “G. d'Annuzio” University

Statistical Methods & Applications, 2001, vol. 10, issue 1, No 7, 67-79

Abstract: Abstract Kernel-based density estimation algorithms are inefficient in presence of discontinuities at support endpoints. This is substantially due to the fact that classic kernel density estimators lead to positive estimates beyond the endopoints. If a nonparametric estimate of a density functional is required in determining the bandwidth, then the problem also affects the bandwidth selection procedure. In this paper algorithms for bandwidth selection and kernel density estimation are proposed for non-negative random variables. Furthermore, the methods we propose are compared with some of the principal solutions in the literature through a simulation study.

Keywords: Boundary bias; Boundary kernel estimators; Kernel density estimation; Local polynomial fit; Plug-in bandwidth selection; Probability integral transformation; Reflection about the boundary (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1007/BF02511640

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