The bias reduction in density estimation using a geometric extrapolated kernel estimator

Year 2018, Volume: 47 Issue: 4, 1003 - 1021, 01.08.2018

Reza Salehi Ali Shadrokh

https://izlik.org/JA64PS72FZ

Abstract

One of the nonparametric methods to estimate the probability density is kernel method. In this paper, kernel density estimation methods including the naive kernel(NK) estimator and geometric extrapolation based kernel(GEBK) method are introduced and discussed. Theoretical properties, including the selection of smoothing parameter, the accuracy of resultant estimators using Monte Carlo simulation are studied. The results show that the amount of bias in the proposed geometric extrapolation based kernel estimator significantly decreases.

Keywords

Kernel density estimation , Bias reduction , Smoothing parameter , Geometric extrapolation

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