This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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302
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #307675 |
Title:
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Wavelet-Based Power Transformation for Long Memory Regression Models with non-Gaussian Errors
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Author(s):
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Kyungduk Ko*+
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Companies:
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Boise State University
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Address:
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1910 University Dr., Boise, ID, 83725-1555,
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Keywords:
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Long memory ;
Box-Cox transform ;
Gibbs sampler
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Abstract:
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We consider a linear regression model with its response variable being non-Gaussian and long range dependent, and perform the Box-Cox transform for achieving approximate normality of the response variable. To this end we propose a wavelet-based estimation method that provides a simultaneous estimation of the model parameters under the assumption of unknown transformation and long memory parameters, a situation which arises commonly in practice. Fractional Gaussian noise and I(d) process are considered for the long range dependent error. We explore a posterior estimation method via a collapsed Gibbs sampler in the wavelet domain. Performances are assessed using simulation studies.
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Authors who are presenting talks have a * after their name.
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