| Issue |
ITM Web Conf.
Volume 23, 2018
XLVIII Seminar of Applied Mathematics
|
|
|---|---|---|
| Article Number | 00018 | |
| Number of page(s) | 5 | |
| DOI | https://doi.org/10.1051/itmconf/20182300018 | |
| Published online | 07 November 2018 | |
The study and comparison of one-dimensional kernel estimators – a new approach. Part 2. A hydrology case study
Department of Mathematics, Wroclaw University of Environmental and Life Sciences Grunwaldzka 53, 50-357 Wroclaw, Poland
* Corresponding author: apm.mich@gmail.com
The main purpose of this article is to present the numerical consequences of selected methods of kernel estimation, using the example of empirical data from a hydrological experiment [1, 2]. In the construction of kernel estimators we used two types of kernels – Gaussian and Epanechnikov – and several methods of selecting the optimal smoothing bandwidth (see Part 1), based on various statistical and analytical conditions [3–6]. Further analysis of the properties of kernel estimators is limited to eight characteristic estimators. To assess the effectiveness of the considered estimates and their similarity, we applied the distance measure of Marczewski and Steinhaus [7]. Theoretical and numerical considerations enable the development of an algorithm for the selection of locally most effective kernel estimators.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.