Bandwidth selection for kernel density estimation with length-biased data
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Título: | Bandwidth selection for kernel density estimation with length-biased data |
Autor/a: | Borrajo García, María Isabel González Manteiga, Wenceslao Martínez Miranda, María Dolores |
Centro/Departamento: | Universidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimización |
Palabras chave: | Bootstrap | Rule-of-thumb | Cross-validation | Nonparametric | Weighted data | |
Data: | 2017 |
Editor: | Taylor & Francis |
Cita bibliográfica: | Borrajo, M. I., González-Manteiga, W., & Martínez-Miranda, M. D. (2017). Bandwidth selection for kernel density estimation with length-biased data. Journal of Nonparametric Statistics, 29(3), 636-668. |
Resumo: | Length-biased data are a particular case of weighted data, which arise in many situations: biomedicine, quality control or epidemiology among others. In this paper we study the theoretical properties of kernel density estimation in the context of length-biased data, proposing two consistent bootstrap methods that we use for bandwidth selection. Apart from the bootstrap bandwidth selectors we suggest a rule-of-thumb. These bandwidth selection proposals are compared with a least-squares cross-validation method. A simulation study is accomplished to understand the behaviour of the procedures in finite samples |
Descrición: | This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Nonparametric Statistics on 23 Jun 2017, available online: https://doi.org/10.1080/10485252.2017.1339309. |
Versión do editor: | https://doi.org/10.1080/10485252.2017.1339309 |
URI: | http://hdl.handle.net/10347/20313 |
DOI: | 10.1080/10485252.2017.1339309 |
ISSN: | 1048-5252 |
E-ISSN: | 1029-0311 |
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