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

Viewed

Total article views: 2,099 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,342 674 83 2,099 67 96
  • HTML: 1,342
  • PDF: 674
  • XML: 83
  • Total: 2,099
  • BibTeX: 67
  • EndNote: 96
Views and downloads (calculated since 13 Feb 2023)
Cumulative views and downloads (calculated since 13 Feb 2023)

Viewed (geographical distribution)

Total article views: 2,099 (including HTML, PDF, and XML) Thereof 2,093 with geography defined and 6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 19 Aug 2025
Download
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.
Share