This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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346
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #308879 |
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Title:
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Properties of a Block Bootstrap Under Long-Range Dependence
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Author(s):
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Young Min Kim*+
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Companies:
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Iowa State University
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Address:
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Department of Statistics & Statistical Laboratory, Ames, IA, 50010,
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Keywords:
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block size ;
confidence interval ;
sample average ;
variance estimation
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Abstract:
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Largely, the block bootstrap has been developed for weakly dependent time processes and, in this context, much research has focused on determining the large-sample properties of block bootstrap inference about sample means. This work validates block bootstrap distribution estimation for stationary, linear processes exhibiting strong dependence. For estimating the sample mean's variance under long memory, explicit expressions are provided for the bias and variance of moving block and non-overlapping block bootstrap estimators, which differ critically from the weak dependence setting. The findings in distribution and variance estimation are illustrated using simulation.
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Authors who are presenting talks have a * after their name.
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