Abstract Details
Activity Number:
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279
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Korean International Statistical Society
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Abstract #311017
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View Presentation
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Title:
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Autocovariance Function Estimation via Penalized Regression
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Author(s):
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Lina Liao*+ and Cheolwoo Park and Jan Hannig and Kee-Hoon Kang
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Companies:
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University of Georgia and University of Georgia and University of North Carolina at Chapel Hill and Hankuk University of Foreign Studies
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Keywords:
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Autocovariance function ;
Asymptotics ;
Penalized regression ;
Time series
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Abstract:
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The work revisits the autocovariance function estimation, a fundamental problem in statistical inference for time series. We convert the function estimation problem into constrained penalized regression with a generalized penalty that provides us with flexible and accurate estimation, and study the asymptotic properties of the proposed estimator. In case of a nonzero time series, we apply a penalized regression technique to a differenced time series, which does not require a separate detrending procedure. In penalized regression, selection of tuning parameters is critical and we propose four different data-driven criteria for determining them. A simulation study shows effectiveness of the tuning parameter selection and that the proposed approach is superior to some existing methods.
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Authors who are presenting talks have a * after their name.
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