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Statistics & Risk Modeling

with Applications in Finance and Insurance

Editor-in-Chief: Stelzer, Robert

4 Issues per year


Cite Score 2016: 0.33

SCImago Journal Rank (SJR) 2016: 0.346
Source Normalized Impact per Paper (SNIP) 2016: 0.167

Mathematical Citation Quotient (MCQ) 2016: 0.32

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2196-7040
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Volume 26, Issue 3

Issues

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

Abstract

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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Citing Articles

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[1]
Khedidja Kebabi and Fatiha Messaci
Statistics & Probability Letters, 2012, Volume 82, Number 11, Page 1908
[2]
Zohra Guessoum and Elias Ould Saïd
Journal of Nonparametric Statistics, 2012, Volume 24, Number 2, Page 379

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