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Bayesian wavelet shrinkage in transformation based linear models
| dc.creator | Ray, Shubhankar | |
| dc.date.accessioned | 2012-06-07T23:17:45Z | |
| dc.date.available | 2012-06-07T23:17:45Z | |
| dc.date.created | 2002 | |
| dc.date.issued | 2002 | |
| dc.identifier.uri | https://hdl.handle.net/1969.1/ETD-TAMU-2002-THESIS-R396 | |
| dc.description | Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item. | en |
| dc.description | Includes bibliographical references (leaves 44-48). | en |
| dc.description | Issued also on microfiche from Lange Micrographics. | en |
| dc.description.abstract | Most of the noise models in signal processing are either additive or multiplicative. However, the widely held wavelet shrinkage estimators for signal denoising deal only with additive noise. In this thesis, a new Bayesian wavelet shrinkage model is presented that encompasses both types of noise as well as noise that may exist between these two extremes. In applications such as SAR Imaging, where multiplicative noise is predominant, statistical models intended for additive noise removal can effect a fair amount of restoration. This leads us to believe that noise in the signal can be considered as somewhere between multiplicative and additive. The new estimator removes noise by better adapting to the noise on hand. This approach is motivated by the work of Pericchi on analysis of Box & Cox transformations in the linear model. In addition, mixture priors governing the transformation are shown useful in predicting the noise from a choice of models. Experimental results are also reported. | en |
| dc.format.medium | electronic | en |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en_US | |
| dc.publisher | Texas A&M University | |
| dc.rights | This thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use. | en |
| dc.subject | electrical engineering. | en |
| dc.subject | Major electrical engineering. | en |
| dc.title | Bayesian wavelet shrinkage in transformation based linear models | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | electrical engineering | en |
| thesis.degree.name | M.S. | en |
| thesis.degree.level | Masters | en |
| dc.type.genre | thesis | en |
| dc.type.material | text | en |
| dc.format.digitalOrigin | reformatted digital | en |
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