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FFT-based fast bandwidth selector for multivariate kernel density estimation

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  • Gramacki, Artur
  • Gramacki, Jarosław

Abstract

The performance of multivariate kernel density estimation (KDE) depends strongly on the choice of bandwidth matrix. The high computational cost required for its estimation provides a big motivation to develop fast and accurate methods. One of such methods is based on the Fast Fourier Transform. However, the currently available implementation works very well only for the univariate KDE and its multivariate extension suffers from a very serious limitation as it can accurately operate only with diagonal bandwidth matrices. A more general solution is presented where the above mentioned limitation is relaxed. Moreover, the presented solution can be easily adopted also for the task of efficient computation of integrated density derivative functionals involving an arbitrary derivative order. Consequently, bandwidth selection for kernel density derivative estimation is also supported. The practical usability of the new solution is demonstrated by comprehensive numerical simulations.

Suggested Citation

  • Gramacki, Artur & Gramacki, Jarosław, 2017. "FFT-based fast bandwidth selector for multivariate kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 27-45.
    • Handle: RePEc:eee:csdana:v:106:y:2017:i:c:p:27-45
    DOI: 10.1016/j.csda.2016.09.001
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    References listed on IDEAS

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    Cited by:

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    2. Federico Palacios-González & Rosa M. García-Fernández, 2020. "A faster algorithm to estimate multiresolution densities," Computational Statistics, Springer, vol. 35(3), pages 1207-1230, September.

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