A Bayesian procedure for bandwidth selection in circular kernel density estimation

Bedouhene Kahina () and Zougab Nabil ()
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Bedouhene Kahina: Department of Mathematics, Mouloud Mammeri University of Tizi-Ouzou, Tizi-Ouzou, Algeria
Zougab Nabil: Department of Technology and Research Unit LaMOS, University of Bejaia, Bejaia, Algeria

Monte Carlo Methods and Applications, 2020, vol. 26, issue 1, 69-82

Abstract: A Bayesian procedure for bandwidth selection in kernel circular density estimation is investigated, when the Markov chain Monte Carlo (MCMC) sampling algorithm is utilized for Bayes estimates. Under the quadratic and entropy loss functions, the proposed method is evaluated through a simulation study and real data sets, which were already discussed in the literature. The proposed Bayesian approach is very competitive in comparison with the existing classical global methods, namely plug-in and cross-validation techniques.

Keywords: Bandwidth selection; Bayesian approach; circular density; cross validation; loss functions; MCMC; plug-in (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1515/mcma-2020-2056

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