On the strong uniform consistency for relative error of the regression function estimator for censoring times series model

Consider a random vector (X, T), where X is d-dimensional and T is one-dimensional. We suppose that the random variable T is subject to random right censoring and satisfies the $\alpha$-mixing property. The aim of this paper is to study the behavior of the kernel estimator of the relative error regr...

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Bibliographic Details
Main Authors Feriel, Bouhadjera, Said, Elias Ould
Format Journal Article
LanguageEnglish
Published 04.10.2019
Subjects
Mathematics - Statistics Theory
Statistics - Applications
Statistics - Computation
Statistics - Theory
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Summary:Consider a random vector (X, T), where X is d-dimensional and T is one-dimensional. We suppose that the random variable T is subject to random right censoring and satisfies the $\alpha$-mixing property. The aim of this paper is to study the behavior of the kernel estimator of the relative error regression and to establish its uniform almost sure consistency with rate. Furthermore, we have highlighted the covariance term which measures the dependency. The simulation study shows that the proposed estimator performs well for a finite sample size in different cases.
DOI:10.48550/arxiv.1910.01964