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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
08/07/1998 |
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Data da última atualização: |
13/12/2010 |
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Autoria: |
BRUCE, A. G.; DONOHO, D. L.; GAO, H. Y.; MARTIN, R. D. |
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Título: |
Smoothing and robust wavelet analysis |
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Ano de publicação: |
1994 |
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Fonte/Imprenta: |
In: DUTTER, R.; GROSSMANN, W. (Ed.). Compstat: proceedings in computational statistics. [Hildeberg]: Physica-Verlag, 1994. |
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Páginas: |
p. 531-547. |
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Idioma: |
Inglês |
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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. |
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Palavras-Chave: |
Computational statistics; Estatistica computacional. |
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Categoria do assunto: |
-- |
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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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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| Registros recuperados : 4 | |
| 3. |  | CRUZ, I.; BRUCE, A.; SEVGAN, S.; AKUTSE, K. S.; MOHAMED, F. S.; NIASSY, S.; RANGASWAMY, M.; SIDHU, J.; GOERGEN, G.; RWOMUSHANA, I.; KASINA, M.; BA, M.; ABOAGYE, E.; STEPHAN, D.; WENNMANN, J.; NEERING, E.; MUSHOBAZI, W. Biological control and biorational pesticides for fall armyworm management. In: PRASANNA, B. M.; HUESING, J. E.; EDDY, R.; PESCHKE, V. M. (Ed.). Fall armyworm in Africa: a guide for integrated pest management. 5. ed. México, DF: CIMMYT, 2018. p. 63-88.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 4. | | CRUZ, I.; BRUCE, A.; SEVGAN, S.; AKUTSE, K. S.; MOHAMED, F. S.; NIASSY, S.; RANGASWAMY, M.; SIDHU, J.; GOERGEN, G.; RWOMUSHANA, I.; KASINA, M.; BA, M.; ABOAGYE, E.; STEPHAN, D.; WENNMANN, J.; NEERING, E.; MUSHOBAZI, W. Lutte biologique et pesticides biorationnaux pour la gestion de la chenille légionnaire d'automne. In: PRASANNA, B. M.; HUESING, J. E.; EDDY, R.; PESCHKE, V. M. (Ed.). La chenille légionnaire d'automne en Afrique: un guide pour une lutte integree contre le ravageur. México, DF: CDMX: CIMMYT, 2018. p. 63-88.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Milho e Sorgo. |
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| Registros recuperados : 4 | |
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