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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Thomas Slawig on behalf of the Authors (13 Sep 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (18 Sep 2023) by Travis O'Brien
AR by Thomas Slawig on behalf of the Authors (06 Oct 2023)  Author's response   Manuscript 
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.