Articles | Volume 16, issue 22
https://doi.org/10.5194/gmd-16-6609-2023
https://doi.org/10.5194/gmd-16-6609-2023
Methods for assessment of models
 | 
16 Nov 2023
Methods for assessment of models |  | 16 Nov 2023

A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research

Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig

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Cited articles

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Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.