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Abstract Details
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Activity Number:
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525
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #300579 |
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Title:
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Asymptotic Normality for the Kernel Estimator of the Regression Function for Censored Time Series
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Author(s):
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Zohra Guessoum*+ and Elias Ould Said
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Companies:
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Laboratoire M.S.T.D and University Lille Nord de France
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Address:
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Faculté de Mathématiques, dépt probabilités , Algiers, 16111, Algeria
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Keywords:
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censored data ;
asymptotic normality ;
strong mixing ;
nonparametric regression
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Abstract:
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In this paper, we consider the estimation of the regression function when the interest variable is subject to random censorship and the data satisfy some dependency conditions. We show that the kernel estimate suitably normalized is asymptotically normally distributed and the asymptotic variance is given explicitly. An application to confidence bands is given. Some simulations are drawn to lend further support to our theoretical results and to compare finite samples sizes with different rates of censoring and dependence.
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