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Activity Number:
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369
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #304206 |
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Title:
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Resampling-Based Bias-Corrected Time Series Prediction
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Author(s):
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Soutir Bandyopadhyay*+
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Companies:
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Texas A&M University
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Address:
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Department of Statistics , College Station, TX, 77843,
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Keywords:
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resampling ; time series
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Abstract:
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In this paper, we consider estimation of the mean squared prediction error (MSPE) of the best linear predictor of functionals of future observations from a stationary time series. We develop a resampling methodology for estimating the MSPE when the unknown parameters in the best linear predictor are estimated. Further, we propose a bias corrected MSPE estimator based on the bootstrap and establish its second order accuracy to get rid of the masking effect. Finite sample properties of the method are investigated through a simulation study.
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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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