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

Authors : Reza SALEHİ, Ali SHADROKH

Pages : 1003-1021

View : 25 | Download : 7

Publication Date : 2018-08-01

Article Type : Research Paper

Abstract :One of the nonparametric methods to estimate the probability density is kernel method. In this paper, kernel density estimation methods including the naive kernelinsert ignore into journalissuearticles values(NK); estimator and geometric extrapolation based kernelinsert ignore into journalissuearticles values(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