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Abstract Details
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Activity Number:
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173
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 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 - #305089 |
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Title:
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Jump detection in time series nonparametric regression models: a polynomial spline approach
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Author(s):
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Qiongxia Song*+ and Yang Yujiao
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Companies:
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The University of Texas at Dallas and East China Normal University
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Address:
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800 W Campbell Road, Richardson, TX, 75080, United States
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Keywords:
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Jump detection ;
time series ;
splines
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Abstract:
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For time series nonparametric regression models with discontinuities, we propose to use polynomial splines to estimate locations and sizes of jumps in the mean function. Under reasonable conditions, test statistics for the existence of jumps are given and their limiting distributions are derived under the null hypothesis that the mean function is smooth. Simulations are provided to check the powers of the tests. A climate data application and an application to the U.S. unemployment rates of men and women are used to illustrate the performance of the proposed method in practice.
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