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

Abramson, I. S.: On bandwidth variation in kernel estimates-a square root law, Ann. Stat., pp. 1217–1223, https://doi.org/10.1214/aos/1176345986, 1982. a, b, c
Berlinet, A.: Hierarchies of higher order kernels, Prob. Theory Rel., 94, 489–504, https://doi.org/10.1007/bf01192560, 1993. a
Bernacchia, A. and Pigolotti, S.: Self-Consistent Method for Density Estimation, J. R. Stat. Soc. B, 73, 407–422, https://doi.org/10.1111/j.1467-9868.2011.00772.x, 2011. a
Boccara, N.: Functional Analysis – An Introduction for Physicists, Academic Press, Inc., ISBN 0121088103, 1990. a
Botev, Z. I., Grotowski, J. F., and Kroese, D. P.: Kernel density estimation via diffusion, Ann. Stat., 38, 2916–2957, https://doi.org/10.1214/10-AOS799, 2010. a, b, c, d, e, f, g, h, i, j, k, l, m, n
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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.
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