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Abstract Details
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Activity Number:
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352
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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| Abstract - #302337 |
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Title:
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Asymptotics for Time-Varying Autoregressive Processes
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Author(s):
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Sreenivas Konda*+
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Companies:
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Temple University
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Address:
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345 Speakman Hall, Philadelphia, 19122,
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Keywords:
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Time series ;
Nonstationary ;
Locally stationary ;
Local likelihood ;
Strong mixing ;
EEG
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Abstract:
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A theoretical framework is proposed for the asymptotics of time-varying autoregressive parameters' estimates in time domain. First we simplify the problem by fitting the time dependent parameters of these nonstationary processes by local polynomial models and then estimate the parameters of these approximate models by weighted least squares method. The asymptotics of the proposed estimators are derived and studied under strong mixing conditions. The developed asymptotics are further investigated and compared using Gaussian errors and Fisher Information Matrix. Local linear models seem to exhibit some optimal properties if bias is corrected or minimized. One useful outcome of this research is to apply minimum biased kernel smoothers for larger bandwidth windows. Such method expected to obtain small standard error values for the estimators, hence the smaller MSE. This research will be presented and explained using EEG data.
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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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