β-divergence loss for the kernel density estimation with bias reduced
In this paper, we investigate the problem of estimating the probability density function. The kernel density estimation with bias reduced is nowadays a standard technique in explorative data analysis, there is still a big dispute on how to assess the quality of the estimate and which choice of bandw...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-07-01
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| Series: | Statistical Theory and Related Fields |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24754269.2020.1858630 |
| _version_ | 1797677039455567872 |
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| author | Hamza Dhaker El Hadji Deme Youssou Ciss |
| author_facet | Hamza Dhaker El Hadji Deme Youssou Ciss |
| author_sort | Hamza Dhaker |
| collection | DOAJ |
| description | In this paper, we investigate the problem of estimating the probability density function. The kernel density estimation with bias reduced is nowadays a standard technique in explorative data analysis, there is still a big dispute on how to assess the quality of the estimate and which choice of bandwidth is optimal. This framework examines the most important bandwidth selection methods for kernel density estimation in the context of with bias reduction. Normal reference, least squares cross-validation, biased cross-validation and β-divergence loss methods are described and expressions are presented. In order to assess the performance of our various bandwidth selectors, numerical simulations and environmental data are carried out. |
| first_indexed | 2024-03-11T22:39:10Z |
| format | Article |
| id | doaj.art-ce2ed3bfed9547c887cf3fbce91f0c63 |
| institution | Directory Open Access Journal |
| issn | 2475-4269 2475-4277 |
| language | English |
| last_indexed | 2024-03-11T22:39:10Z |
| publishDate | 2021-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Statistical Theory and Related Fields |
| spelling | doaj.art-ce2ed3bfed9547c887cf3fbce91f0c632023-09-22T09:19:46ZengTaylor & Francis GroupStatistical Theory and Related Fields2475-42692475-42772021-07-015322123110.1080/24754269.2020.18586301858630β-divergence loss for the kernel density estimation with bias reducedHamza Dhaker0El Hadji Deme1Youssou Ciss2Universite de MonctonUniversite Gaston BergerUniversite Gaston BergerIn this paper, we investigate the problem of estimating the probability density function. The kernel density estimation with bias reduced is nowadays a standard technique in explorative data analysis, there is still a big dispute on how to assess the quality of the estimate and which choice of bandwidth is optimal. This framework examines the most important bandwidth selection methods for kernel density estimation in the context of with bias reduction. Normal reference, least squares cross-validation, biased cross-validation and β-divergence loss methods are described and expressions are presented. In order to assess the performance of our various bandwidth selectors, numerical simulations and environmental data are carried out.http://dx.doi.org/10.1080/24754269.2020.1858630bandwidthβ-divergencenonparametric estimationbias reductionenvironmental data |
| spellingShingle | Hamza Dhaker El Hadji Deme Youssou Ciss β-divergence loss for the kernel density estimation with bias reduced Statistical Theory and Related Fields bandwidth β-divergence nonparametric estimation bias reduction environmental data |
| title | β-divergence loss for the kernel density estimation with bias reduced |
| title_full | β-divergence loss for the kernel density estimation with bias reduced |
| title_fullStr | β-divergence loss for the kernel density estimation with bias reduced |
| title_full_unstemmed | β-divergence loss for the kernel density estimation with bias reduced |
| title_short | β-divergence loss for the kernel density estimation with bias reduced |
| title_sort | β divergence loss for the kernel density estimation with bias reduced |
| topic | bandwidth β-divergence nonparametric estimation bias reduction environmental data |
| url | http://dx.doi.org/10.1080/24754269.2020.1858630 |
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