<b>Proposal of a bootstrap procedure using measures of influence in non-linear regression models with outliers</b> - doi: 10.4025/actascitechnol.v36i1.17564

Autores

  • Larissa Ribeiro de Andrade Universidade Federal de Lavras
  • Marcelo Angelo Cirillo Universidade Federal de Lavras
  • Luiz Alberto Beijo Universidade Federal de Alfenas

DOI:

https://doi.org/10.4025/actascitechnol.v36i1.17564

Palavras-chave:

CovRatio, accuracy, precision, Monte Carlo

Resumo

The bootstrap method is generally performed by presupposing that each sample unit would show the same probability of being re-sampled. However, when a sample with outliers is taken into account, the empirical distribution generated by this method may be influenced, or rather, it may not accurately represent the original sample. Current study proposes a bootstrap algorithm that allows the use of measures of influence in the calculation of re-sampling probabilities. The method was reproduced in simulation scenarios taking into account the logistic growth curve model and the CovRatio measurement to evaluate the impact of an influential observation in the determinacy of the matrix of the co-variance of parameter estimates. In most cases, bias estimates were reduced. Consequently, the method is suitable to be used in non-linear models and allows the researcher to apply other measures for better bias reductions.

 

 

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Biografia do Autor

Larissa Ribeiro de Andrade, Universidade Federal de Lavras

Doutoranda em estatí­stica e experimentação agropecuária.

Titulação: Mestre em estatí­stica e experimentação agropecuária

Marcelo Angelo Cirillo, Universidade Federal de Lavras

Prof. Adjunto ní­vel III - Departamento de Ciências Exatas - Universidade Federal de Lavras

Luiz Alberto Beijo, Universidade Federal de Alfenas

Prof. Doutor em estatí­stica e experimentação agropecuária. Atuante como prof. orientador na pós-graduação em

Publicado

2014-01-07

Como Citar

Andrade, L. R. de, Cirillo, M. A., & Beijo, L. A. (2014). <b>Proposal of a bootstrap procedure using measures of influence in non-linear regression models with outliers</b> - doi: 10.4025/actascitechnol.v36i1.17564. Acta Scientiarum. Technology, 36(1), 93–99. https://doi.org/10.4025/actascitechnol.v36i1.17564

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0.8
2019CiteScore
 
 
36th percentile
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0.8
2019CiteScore
 
 
36th percentile
Powered by  Scopus

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