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

Reza Salehi | Zendy; Ali Shadrokh | Zendy

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

Author(s) -

Reza Salehi,

Ali Shadrokh

Publication year - 2016

Publication title -

hacettepe journal of mathematics and statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.312

H-Index - 26

ISSN - 1303-5010

DOI - 10.15672/hjms.201614922002

Subject(s) - mathematics , estimator , kernel density estimation , kernel (algebra) , reduction (mathematics) , density estimation , statistics , estimation , multivariate kernel density estimation , variable kernel density estimation , kernel method , geometry , pure mathematics , artificial intelligence , computer science , support vector machine , management , economics

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.

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