Bayesian nonparametric estimation of bandwidth using mixtures of kernel estimators for length-biased data

Kernel density estimation has been applied in many computational subjects. In this paper, we propose a density estimation procedure from a Bayesian nonparametric perspective using Dirichlet process prior for the length-biased data under an unknown kernel function. In this situation, the kernel withi...

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Bibliographic Details
Published inJournal of statistical computation and simulation Vol. 90; no. 10; pp. 1849 - 1874
Main Authors Rahnamay Kordasiabi, S., Khazaei, S.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 02.07.2020
Taylor & Francis Ltd
Subjects
bandwidth selection
Bandwidths
Bayesian analysis
Bayesian nonparametric
Computer simulation
Density
Dirichlet problem
Dirichlet process prior
kernel estimator
Kernel functions
Length-biased data analysis
Markov analysis
Markov chains
Nonparametric statistics
Parameter estimation
pivoting method
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Summary:Kernel density estimation has been applied in many computational subjects. In this paper, we propose a density estimation procedure from a Bayesian nonparametric perspective using Dirichlet process prior for the length-biased data under an unknown kernel function. In this situation, the kernel within the Dirichlet process mixture model will be approximated by the kernel density estimator. We present a Bayesian nonparametric method for finding the bandwidth parameter in the kernel density estimation using a Markov chain Monte Carlo approach. Then, this approach is used to the simulated and real data set. Finally, we compare the proposed bandwidth estimation with the other estimations like cross-validation and Bayes based on the mean integrated squared error criterion.
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ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2020.1750613