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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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Embrapa Agricultura Digital (CNPTIA) |
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| 1. |  | SANCHEZ, P. A.; AHAMED, S.; CARRÉ, F.; HARTEMINK, A. E.; HEMPEL, J.; HUISING, J.; LAGACHERIE, P.; MACBRATNEY, A. B.; MCKENZIE, N. J.; MENDONÇA-SANTOS, M. de L.; MINASNY, B.; MONTANARELLA, L.; OKOTH, P.; PALM, C. A.; SACHS, J. D.; SHEPHERD, K. D.; VAGEN, T. G.; VANLAUWE, B.; WALSH, M. G.; WINOWIECKI, L. A.; ZHANG, G. L. Digital soil map of the world. Science, v. 325, n. 5941, p. 680-681, 2009.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
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