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Journals / Hacettepe Journal of Mathematics and Statistics / 2018 / Cilt: 47 - Sayı: 2

Density estimation of circular data with Bernstein polynomials

M. P. WİPER
J. A. CARNİCERO
M. C. Ausín
Pages
273–286
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

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

Finally our method is illustrated with a simulation study and a real data example.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper introduces a new, non-parametric approach to the modeling of circular data, based on the use of Bernstein polynomial densities.
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Key Findings & Data:The model generalizes the standard Bernstein polynomial model to account for the specific characteristics of circular data.
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Conclusion & Impact:Finally our method is illustrated with a simulation study and a real data example.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:circular data · non-parametric modeling · Bernstein polynomials

Abstract

This paper introduces a new, non-parametric approach to the modeling of circular data, based on the use of Bernstein polynomial densities. The model generalizes the standard Bernstein polynomial model to account for the specific characteristics of circular data. In particular, it is shown that the trigonometric moments of the proposed circular Bernstein polynomial distribution can all be derived in closed form. Secondly, we introduce an approach to circular Bernstein polynomial density estimation given a sample of data and examine the properties of this estimator. Finally our method is illustrated with a simulation study and a real data example.

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Cite This Article

WİPER, M. P., CARNİCERO, J. A., Ausín, M. C. (2022). Density estimation of circular data with Bernstein polynomials. Hacettepe Journal of Mathematics and Statistics, Cilt: 47 - Sayı: 2, 273–286. https://ajindex.com/articles/529883

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