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Abstract Details
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Activity Number:
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567
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #305370 |
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Title:
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Online Kernel Density Estimation for Nonstationary Time Series
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Author(s):
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Yinxiao Huang*+
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Companies:
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University of Illinois at Urbana-Champaign
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Address:
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1369 E Hyde Park Blvd, Chicago, IL, 60615, United States
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Keywords:
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kernel estimation ;
online learning ;
time series ;
nonparametric ;
time-dynamic ;
nonstationary
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Abstract:
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Online learning is concerned with the task of making real-time updates as new observations become available. In recent years, with the increasing prevalence of data-streaming sources where large data comes in a sequential manner, and the underlying density is evolving in time, the importance of dynamic estimation with the goal of capturing changing trends and features in distributions is increasingly recognized. In this talk, we shall explore online kernel estimation for a general class of nonstationary nonlinear time series under the dependence structure developed by Wu (2005). We shall discuss the asymptotic behavior of the online kernel density estimator. Bandwidth selection is addressed in terms of minimizing the intergrated MSE. Some simulation work will also be presented.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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