Multiplicative bias correction for discrete kernels

Lynda Harfouche (), Smail Adjabi (), Nabil Zougab () and Benedikt Funke ()
Additional contact information
Lynda Harfouche: University of Bejaia
Smail Adjabi: University of Bejaia
Nabil Zougab: University of Bejaia
Benedikt Funke: Technical University of Dortmund

Statistical Methods & Applications, 2018, vol. 27, issue 2, No 7, 253-276

Abstract: Abstract In this paper, we prove that two multiplicative bias correction techniques (MBC) can be applied for discrete kernels in the context of probability mass function estimation. First, some properties of the MBC discrete kernel estimators (bias, variance and mean integrated squared error) are investigated. Second, the popular cross-validation technique is adapted for bandwidth selection. Finally, a simulation study and a real data application for discrete data illustrate the performance of the MBC estimators based on dirac discrete uniform and triangular discrete kernels.

Keywords: Bandwidth selection; Cross-validation; Discrete kernel; Discrete data; Mean integrated squared error; Multiplicative bias correction (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10260-017-0395-x

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