Abstract Details
Activity Number:
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532
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 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 - #309989 |
Title:
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Rank-Based Estimator in Two-Phase Linear Model
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Author(s):
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Brice Merlin Nguelifack*+
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Companies:
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Auburn University
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Keywords:
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Two-phase ;
Linear ;
Model
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Abstract:
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This paper considers two-phase random design linear models with arbitrary error densities and where the regression function has fixed jump at the true change-point. It obtains the consistency, and the limiting distributions of the $R-$estimators of the underlying parameters in these models. The left end point of the minimizing interval with respect to the change point, herein called the $R-$estimator $\hat{r}_n$ of the change-point parameter $r$ is shown to be $n$-consistent and the underlying $R$-process, as a process in the standardized change-point parameter, is shown to converge weakly to a compound poisson process. This process obtains maximum over a bounded interval and $n(\hat{r}_n -r)$ converges weakly to the left end point of this interval. These results are different from those available in the literature for the case of two-phase linear regression models when jump sizes tends to zero as $n$ tends to infinity.
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Authors who are presenting talks have a * after their name.
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