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Semi-recursive kernel conditional density estimators under random censorship and dependent data
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.
Keywords
Asymptotic normality, censored data, conditional density, mixing sequences, recursive kernel estimators, survival data, strong consistency
