Semi-recursive kernel conditional density estimators under random censorship and dependent data

Ali Laksaci, Salah Khardani and Sihem Semmar

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 7, 2116-2138

Abstract: In this work, we extend to the case of the strong mixing data the results of Khardani and Semmar. A kernel-type recursive estimator of the conditional density function is introduced. We study the properties of these estimators and compare them with Rosemblatt’s nonrecursive estimator. Then, a strong consistency rate as well as the asymptotic distribution of the estimator are established under an α-mixing condition. A simulation study is considered to show the performance of the proposed estimator.

Date: 2022
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DOI: 10.1080/03610926.2020.1764038

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