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Journals / Hacettepe Journal of Mathematics and Statistics / 2026 / Volume: 39 Issue: 3

ON THE ADAPTIVE NADARAYA-WATSON KERNEL REGRESSION ESTIMATORS

S. Demir
Ö. Toktamiş
Pages
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Publication Date
2010-03-01
DOI
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AI Summary & Key Takeaways

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⚡ TL;DR(Too Long; Didn't Read)

The results of the simulation study show that the adaptive Nadaraya-Watson kernel estimators have better performance than the kernel estimations with fixed bandwidth.

📌 3 Key Takeaways

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Objective & Hypothesis:Nonparametric kernel estimators are widely used in many research areas of statistics.
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Key Findings & Data:An important nonparametric kernel estimator of a regression function is the Nadaraya-Watson kernel regression estimator which is often obtained by using a fixed bandwidth.
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Conclusion & Impact:The results of the simulation study show that the adaptive Nadaraya-Watson kernel estimators have better performance than the kernel estimations with fixed bandwidth.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Nonparametric regression · Nadaraya-Watson kernel estimator · Adaptive kernel estimation

Abstract

Nonparametric kernel estimators are widely used in many research areas of statistics. An important nonparametric kernel estimator of a regression function is the Nadaraya-Watson kernel regression estimator which is often obtained by using a fixed bandwidth. However, the adaptive kernel estimators with varying bandwidths are specially used to estimate density of the long-tailed and multi-mod distributions. In this paper, we consider the adaptive Nadaraya-Watson kernel regression estimators. The results of the simulation study show that the adaptive Nadaraya-Watson kernel estimators have better performance than the kernel estimations with fixed bandwidth.

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Cite This Article

Demir, S., Toktamiş, Ö. (2010). ON THE ADAPTIVE NADARAYA-WATSON KERNEL REGRESSION ESTIMATORS. Hacettepe Journal of Mathematics and Statistics, Volume: 39 Issue: 3. https://ajindex.com/articles/1403702

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References (8)

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