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Statistics & Risk Modeling

with Applications in Finance and Insurance

Editor-in-Chief: Stelzer, Robert

4 Issues per year

Cite Score 2017: 0.96

SCImago Journal Rank (SJR) 2017: 0.455
Source Normalized Impact per Paper (SNIP) 2017: 0.853

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Volume 26, Issue 3


On nonparametric estimation of the regression function under random censorship model

Zohra Guessoum / Elias Ould-Said
Published Online: 2009-09-25 | DOI: https://doi.org/10.1524/stnd.2008.0919


In this paper, we study the behavior of a kernel estimator for the regression function in a random right-censoring model. We establish pointwise and uniform strong consistency over a compact set and give a rate of convergence for the estimate.The asymptotic normality of the estimate is also proved. Simulations are drawn for different cases to illustrate both, convergence and asymptotic normality.

Keywords: asymptotic normality; censored data; Kaplan-Meier estimator; kernel; nonparametric regression

About the article

* Correspondence address: L. M. P. A. J. Liouville, Université du Littoral Côte dOpale, BP 699, 62228 Calais, Frankreich,

Published Online: 2009-09-25

Published in Print: 2009-04-01

Citation Information: Statistics & Decisions International mathematical journal for stochastic methods and models, Volume 26, Issue 3, Pages 159–177, ISSN (Print) 0721-2631, DOI: https://doi.org/10.1524/stnd.2008.0919.

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