Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 22
Articles | Volume 16, issue 22
https://doi.org/10.5194/gmd-16-6609-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-6609-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Viewed

Total article views: 1,093 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
698 345 50 1,093 32 33
  • HTML: 698
  • PDF: 345
  • XML: 50
  • Total: 1,093
  • BibTeX: 32
  • EndNote: 33
Views and downloads (calculated since 13 Feb 2023)
Cumulative views and downloads (calculated since 13 Feb 2023)

Viewed (geographical distribution)

Total article views: 1,093 (including HTML, PDF, and XML) Thereof 1,073 with geography defined and 20 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 07 May 2024
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.
Read more