Bandwidth selection for kernel density estimation with length-biased data

Martinez-Miranda, M. D., Gonzalez-Manteiga, W & Borrajo, MI (2017). Bandwidth selection for kernel density estimation with length-biased data. Journal of Nonparametric Statistics,

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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.

Item Type: Article
Additional Information: This is an Accepted Manuscript of an article that has been accepted for publiblication in Journal of Nonparametric Statistics, published by Taylor & Francis.
Uncontrolled Keywords: Bootstrap, Rule-of-thumb, Cross-validation, Non-parametric, Weighted data
Subjects: H Social Sciences > HA Statistics
Divisions: Cass Business School > Faculty of Actuarial Science & Insurance

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