Abstract Details
Activity Number:
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548
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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 - #310450 |
Title:
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Nonparametric Intervention Time Series Modeling
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Author(s):
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Jin-Hong Park*+
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Companies:
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College of Charleston
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Keywords:
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nonparametric intervention analysis ;
central mean subspace in time series ;
event study ;
dimension reduction ;
Nadaraya-Watson kernel estimator
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Abstract:
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Time series are often affected by interventions such as strikes, earthquakes, or policy changes. In this talk, I introduce a practical nonparametric intervention model using the central mean subspace in time series. I estimate the central mean subspace for time series taking into account known interventions by using the Nadaraya Watson kernel estimator. I use the modified Bayesian information criterion to estimate the unknown lag and dimension. Finally, I demonstrate that this nonparametric approach for intervened time series performs well in simulations and in a real data analysis such as the Monthly average of the oxidant.
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Authors who are presenting talks have a * after their name.
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