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Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
08/07/1998 |
Data da última atualização: |
13/12/2010 |
Autoria: |
BRUCE, A. G.; DONOHO, D. L.; GAO, H. Y.; MARTIN, R. D. |
Título: |
Smoothing and robust wavelet analysis |
Ano de publicação: |
1994 |
Fonte/Imprenta: |
In: DUTTER, R.; GROSSMANN, W. (Ed.). Compstat: proceedings in computational statistics. [Hildeberg]: Physica-Verlag, 1994. |
Páginas: |
p. 531-547. |
Idioma: |
Inglês |
Conteúdo: |
In a series of papers, Donoho and Johnstone develop a powerful theory based on wavelets for extracting non-smooth signals from noisy data. Several nonlinear smoothing algorithms are presented which provide high performance for removing Gaussian noise from a wide range of spatially inhomogeneous signals. However, like other methods based on the linear wavelet transform, these algorithms are very sensitive to certain types of non-Gaussian noise, such as outliers. In this paper, we develop outlier resistant wavelet transforms. In these transforms outliers and outlier patches are localized to just a few scales. By using the outlier resistant wavelet transfoms, we improve upon the Donoho and Johnstone nonlinear signal extraction methods. The outlier resistant wavelet algorithms are included with the S+WAVELETS object-oriented toolkit for wavelet analysis. |
Palavras-Chave: |
Computational statistics; Estatistica computacional. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01442naa a2200193 a 4500 001 1006430 005 2010-12-13 008 1994 bl uuuu u00u1 u #d 100 1 $aBRUCE, A. G. 245 $aSmoothing and robust wavelet analysis 260 $c1994 300 $ap. 531-547. 520 $aIn a series of papers, Donoho and Johnstone develop a powerful theory based on wavelets for extracting non-smooth signals from noisy data. Several nonlinear smoothing algorithms are presented which provide high performance for removing Gaussian noise from a wide range of spatially inhomogeneous signals. However, like other methods based on the linear wavelet transform, these algorithms are very sensitive to certain types of non-Gaussian noise, such as outliers. In this paper, we develop outlier resistant wavelet transforms. In these transforms outliers and outlier patches are localized to just a few scales. By using the outlier resistant wavelet transfoms, we improve upon the Donoho and Johnstone nonlinear signal extraction methods. The outlier resistant wavelet algorithms are included with the S+WAVELETS object-oriented toolkit for wavelet analysis. 653 $aComputational statistics 653 $aEstatistica computacional 700 1 $aDONOHO, D. L. 700 1 $aGAO, H. Y. 700 1 $aMARTIN, R. D. 773 $tIn: DUTTER, R.; GROSSMANN, W. (Ed.). Compstat: proceedings in computational statistics. [Hildeberg]: Physica-Verlag, 1994.
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Embrapa Agricultura Digital (CNPTIA) |
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