A fast and objective multidimensional kernel density estimation method: fastKDE

O’Brien, Travis A., Karthik Kashinath, Nicholas R. Cavanaugh, William D. Collins and O’Brien, John P.

Computational Statistics & Data Analysis, 2016, vol. 101, issue C, 148-160

Abstract: Numerous facets of scientific research implicitly or explicitly call for the estimation of probability densities. Histograms and kernel density estimates (KDEs) are two commonly used techniques for estimating such information, with the KDE generally providing a higher fidelity representation of the probability density function (PDF). Both methods require specification of either a bin width or a kernel bandwidth. While techniques exist for choosing the kernel bandwidth optimally and objectively, they are computationally intensive, since they require repeated calculation of the KDE. A solution for objectively and optimally choosing both the kernel shape and width has recently been developed by Bernacchia and Pigolotti (2011). While this solution theoretically applies to multidimensional KDEs, it has not been clear how to practically do so.

Keywords: Empirical characteristic function; ECF; Kernel density estimation; Histogram; Nonuniform FFT; NuFFT; Multidimensional; KDE (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:101:y:2016:i:c:p:148-160

DOI: 10.1016/j.csda.2016.02.014

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