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Fourier series-based direct plug-in bandwidth selectors for kernel density estimation

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  • Carlos Tenreiro

Abstract

A class of Fourier series-based direct plug-in bandwidth selectors for kernel density estimation is considered in this paper. The proposed bandwidth estimators have a relative convergence rate n−1/2 whenever the underlying density is smooth enough and the simulation results testify that they present a very good finite sample performance against the most recommended bandwidth selection methods in the literature.

Suggested Citation

  • Carlos Tenreiro, 2011. "Fourier series-based direct plug-in bandwidth selectors for kernel density estimation," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 533-545.
    • Handle: RePEc:taf:gnstxx:v:23:y:2011:i:2:p:533-545
    DOI: 10.1080/10485252.2010.537337
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    Cited by:

    1. He, Yaoyao & Zheng, Yaya, 2018. "Short-term power load probability density forecasting based on Yeo-Johnson transformation quantile regression and Gaussian kernel function," Energy, Elsevier, vol. 154(C), pages 143-156.
    2. He, Yaoyao & Wang, Yun & Wang, Shuo & Yao, Xin, 2022. "A cooperative ensemble method for multistep wind speed probabilistic forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    3. He, Yaoyao & Xu, Qifa & Wan, Jinhong & Yang, Shanlin, 2016. "Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function," Energy, Elsevier, vol. 114(C), pages 498-512.
    4. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).

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