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Recursive kernel density estimators under missing data

Yousri Slaoui

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 18, 9101-9125

Abstract: In this article, we propose an automatic bandwidth selection of the recursive kernel density estimators with missing data in the context of global and local density estimation. We showed that, using the selected bandwidth and a special stepsize, the proposed recursive estimators outperformed the non recursive ones in terms of estimation error in the case of global estimation. However, the recursive estimators are much better in terms of computational costs. We corroborated these theoretical results through simulation studies and on the simulated data of the Aquitaine cohort of HIV$\texttt {HIV}$-1-infected patients and on the Coriell cell lines using the chromosome number 11.

Date: 2017
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DOI: 10.1080/03610926.2016.1205618

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