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On nonparametric estimation of the regression function under random censorship model
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Zohra Guessoum
Published/Copyright:
September 25, 2009
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
Published Online: 2009-09-25
Published in Print: 2009-04
© by Oldenbourg Wissenschaftsverlag, München, Germany
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- On nonparametric estimation of the regression function under random censorship model
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- Improved estimation for elliptically symmetric distributions with unknown block diagonal covariance matrix
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Keywords for this article
asymptotic normality;
censored data;
Kaplan-Meier estimator;
kernel;
nonparametric regression
Articles in the same Issue
- On nonparametric estimation of the regression function under random censorship model
- Estimation of optimal portfolio compositions for Gaussian returns
- Improved estimation for elliptically symmetric distributions with unknown block diagonal covariance matrix
- A Bayesian approach to incorporate model ambiguity in a dynamic risk measure
