FFT-based fast bandwidth selector for multivariate kernel density estimation

Artur Gramacki and Jarosław Gramacki

Computational Statistics & Data Analysis, 2017, vol. 106, issue C, 27-45

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.

Keywords: Multivariate kernel density estimation; Density derivative functionals; Bandwidth selection; Fast Fourier Transform; Nonparametric estimation (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:106:y:2017:i:c:p:27-45

DOI: 10.1016/j.csda.2016.09.001

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