Abstract Details
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Activity Number:
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613
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Type:
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Invited
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Date/Time:
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Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #307335 |
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Title:
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Oracally Efficient Estimation of ARMA Model in the Presence of Trend
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Author(s):
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Qin Shao and Lijian Yang*+
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Companies:
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University of Toledo and Michigan State University
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Keywords:
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Autoregressive moving-average ;
B-splines ;
Maximum likelihood estimator ;
Mixing ;
Oracle efficiency
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Abstract:
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Maximum likelihood estimators of the autoregressive and moving-average coefficients when the observed time series contains trend is proposed based on detrended residual sequence, with the trend function estimated by means of B-splines and subtracted from the observations. Oracle efficiency of the estimators is established in the sense that they are as efficient as if the true trend function is given and removed to obtain the mean zero autoregressive moving-average time series. The performance of the estimators is illustrated by simulation studies and real data analysis.
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Authors who are presenting talks have a * after their name.
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