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Abstract Details
Activity Number:
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309
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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Abstract - #300600 |
Title:
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Estimation Methods for Nonlinear Time Series
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Author(s):
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Candace Metoyer*+ and Prabir Burman
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Companies:
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Intel Corporation and University of California at Davis
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Address:
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, Santa Clara, CA, 95054,
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Keywords:
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nonlinear time series ;
state space models ;
penalized likelihood ;
asymptotic mean square error ;
poisson ;
bernoulli
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Abstract:
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We consider the class of structural models for nonlinear time series where the underlying signal may contain trend and seasonal components. In particular, we investigate signal estimation methods for time series whose observations come from a distribution that is a member of the exponential family of distributions. Common examples of these data include Poisson time series (which arise from count data) and Bernoulli time series (which arise from binary response data). A method based on penalized log-likelihood is used to generate estimates of the signal components. Asymptotic results for the mean square error of the estimators are given and applications to real time series data are provided.
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