Nonparametric regression estimation using recursive kernel for dependent censored data
This paper studies a recursive kernel regression method adapted to censored data. We employ a recursive version of the Nadaraya-Watson estimator in this context. The almost sure convergence and the asymptotic normality of the proposed estimator are established under strong mixing conditions.
Efficiency of the method is evaluated through simulations and a real data set study. Our study shows that our proposed method works well within the framework of a data stream.
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