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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 - #304197 |
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Title:
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Bayesian Wavelet-Based Transformation for Inducing Normality from Non-Gaussian Long Memory Time Series
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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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Box-Cox Transformation ; Discrete Wavelet Transform ; MCMC ; Long Memory
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Abstract:
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This paper proposes a wavelet-based Bayesian power transformation procedure through the well known Box-Cox transformation to induce normality from non-Gaussian long memory processes. We consider power transformations of non-Gaussian long memory time series under the assumption of an unknown transformation parameter, a situation which arises commonly in practice, while most research has been devoted to nonlinear transformations of Gaussian long memory time series with known transformation parameter. Specially, this study is mainly focused on the simultaneous estimation of the transformation parameter and long memory parameter.
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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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