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Journals / Hacettepe Journal of Mathematics and Statistics / 2018 / Cilt: 47 - Sayı: 4

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

Reza SALEHİ
Ali SHADROKH
Pages
1003–1021
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that the amount of bias in the proposed geometric extrapolation based kernel estimator significantly decreases.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:One of the nonparametric methods to estimate the probability density is kernel method.
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Key Findings & Data:In this paper, kernel density estimation methods including the naive kernel(NK) estimator and geometric extrapolation based kernel(GEBK) method are introduced and discussed.
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Conclusion & Impact:The results show that the amount of bias in the proposed geometric extrapolation based kernel estimator significantly decreases.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Kernel density estimation · Bias reduction · Smoothing parameter

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.

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Cite This Article

SALEHİ, R., SHADROKH, A. (2022). The bias reduction in density estimation using a geometric extrapolated kernel estimator. Hacettepe Journal of Mathematics and Statistics, Cilt: 47 - Sayı: 4, 1003–1021. https://ajindex.com/articles/529895

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