Communications for Statistical Applications and Methods

  • 제31권3호
  • /
  • Pages.337-347
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  • 2024
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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The shifted Chebyshev series-based plug-in for bandwidth selection in kernel density estimation

  • 투고 : 2023.09.17
  • 심사 : 2024.02.12
  • 발행 : 2024.05.31
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⟨ 이전 논문 다음 논문 ⟩

초록

Kernel density estimation is a prevalent technique employed for nonparametric density estimation, enabling direct estimation from the data itself. This estimation involves two crucial elements: selection of the kernel function and the determination of the appropriate bandwidth. The selection of the bandwidth plays an important role in kernel density estimation, which has been developed over the past decade. A range of methods is available for selecting the bandwidth, including the plug-in bandwidth. In this article, the proposed plug-in bandwidth is introduced, which leverages shifted Chebyshev series-based approximation to determine the optimal bandwidth. Through a simulation study, the performance of the suggested bandwidth is analyzed to reveal its favorable performance across a wide range of distributions and sample sizes compared to alternative bandwidths. The proposed bandwidth is also applied for kernel density estimation on real dataset. The outcomes obtained from the proposed bandwidth indicate a favorable selection. Hence, this article serves as motivation to explore additional plug-in bandwidths that rely on function approximations utilizing alternative series expansions.

키워드

과제정보

The authors would like to thank Kasetsart University and Rajamangala University of Technology Rattanakosin for the support.

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