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| 180. |  | CAPRIO, M.; STORER, N. P.; SISTERSON, M. S.; PECK, S. L.; MAIA, A. de H. N. Assessing the risk of the evolution of resistance to pesticides using spatially complex simulation models. In: WHALON, M. E.; MOTA-SANCHEZ, D.; HOLLINGWORTH, R. M. (Ed.). Global pesticide resistance in arthropods. Cambridge, MA: CABI Publishing, 2008. p.90-117.| Biblioteca(s): Embrapa Meio Ambiente. |
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| Registros recuperados : 345 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Cerrados. Para informações adicionais entre em contato com cpac.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
08/12/2016 |
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Data da última atualização: |
06/02/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
B - 1 |
|
Autoria: |
PARAIBA, C. C. M.; DINIZ, C. A. R.; MAIA, A. de H. N.; RODRIGUES, L. N. |
|
Afiliação: |
CAROLINA COSTA MOTA PARAIBA, UFSCAR; CARLOS ALBERTO RIBEIRO DINIZ, UFSCAR; ALINE DE HOLANDA NUNES MAIA, CNPMA; LINEU NEIVA RODRIGUES, CPAC. |
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Título: |
Truncated location-scale nonlinear regression models. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Communications in Statistics, Theory and Methods (Online), 20 jul 2016. |
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DOI: |
http://dx.doi.org/10.1080/03610926.2016.1152485 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: We present a class of truncated nonlinear regression models for location and scale where the truncated nature of the data is incorporated into the statistical model by assuming that the response variable follows a truncated distribution. The location parameter of the response variable is assumed to be modeled by a continuous nonlinear function of covariates and unknown parameters. In addition, the proposed model also allows for the scale parameter of the responses to be characterized by a continuous function of the covariates and unknown parameters. Three particular cases of the proposed models are presented by considering the response variable to follow a truncated normal, truncated skew normal, and truncated beta distribution. These truncated nonlinear regression models are constructed assuming fixed known truncation limits and model parameters are estimated by direct maximization of the log-likelihood using a nonlinear optimization algorithm. Standardized residuals and diagnostic metrics based on the cases deletion are considered to verify the adequacy of the model and to detect outliers and influential observations. Results based on simulated data are presented to assess the frequentist properties of estimates and a real data set on soil-water retention from the Buriti Vermelho River Basin database is analyzed using the proposed methodology. |
|
Palavras-Chave: |
Análise diagnóstica; Dados de retenção de água do solo; Diagnostic analysis; Nonlinear regression; Regressão não-linear; Soil-water retention data; Truncamento; Truncation. |
|
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02197naa a2200265 a 4500
001 2058239
005 2017-02-06
008 2016 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1080/03610926.2016.1152485$2DOI
100 1 $aPARAIBA, C. C. M.
245 $aTruncated location-scale nonlinear regression models.
260 $c2016
520 $aAbstract: We present a class of truncated nonlinear regression models for location and scale where the truncated nature of the data is incorporated into the statistical model by assuming that the response variable follows a truncated distribution. The location parameter of the response variable is assumed to be modeled by a continuous nonlinear function of covariates and unknown parameters. In addition, the proposed model also allows for the scale parameter of the responses to be characterized by a continuous function of the covariates and unknown parameters. Three particular cases of the proposed models are presented by considering the response variable to follow a truncated normal, truncated skew normal, and truncated beta distribution. These truncated nonlinear regression models are constructed assuming fixed known truncation limits and model parameters are estimated by direct maximization of the log-likelihood using a nonlinear optimization algorithm. Standardized residuals and diagnostic metrics based on the cases deletion are considered to verify the adequacy of the model and to detect outliers and influential observations. Results based on simulated data are presented to assess the frequentist properties of estimates and a real data set on soil-water retention from the Buriti Vermelho River Basin database is analyzed using the proposed methodology.
653 $aAnálise diagnóstica
653 $aDados de retenção de água do solo
653 $aDiagnostic analysis
653 $aNonlinear regression
653 $aRegressão não-linear
653 $aSoil-water retention data
653 $aTruncamento
653 $aTruncation
700 1 $aDINIZ, C. A. R.
700 1 $aMAIA, A. de H. N.
700 1 $aRODRIGUES, L. N.
773 $tCommunications in Statistics, Theory and Methods (Online), 20 jul 2016.
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