Articles | Volume 16, issue 22
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

Data sets

A global marine particulate organic carbon-13 isotope data product Maria-Theresia Verwega, Christopher J. Somes, Robyn E. Tuerena, Anne Lorrain

Model code and software

Diffusion-based kernel density estimator (diffKDE) M.-T. Pelz and T. Slawig

Short summary
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.