Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/50190
Título: Métodos para detecção de Outliers multivariados: Via uso dos estimadores robustos
Título(s) alternativo(s): Methods for detection of multivariate outliers: via the use of robust estimators
Autores: Ferreira, Daniel Furtado
Ferreira, Daniel Furtado
Nogueira, Denismar Alves
Beijo, Luiz Alberto
Batista, Ben Deivide de Oliveira
Palavras-chave: Estimador robusto comedian
Comedian robust estimator
Principal components for detection of outliers (PCOut)
Estimador Ortogonalizado de Gnanadesikan-Kettenring (OGK)
Data do documento: 10-Jun-2022
Editor: Universidade Federal de Lavras
Citação: MARTINS, H. M. Métodos para detecção de Outliers multivariados: Via uso dos estimadores robustos. 2022. 90 p. Dissertação (Mestrado em Estatística e Experimentação Agropecuária) - Universidade Federal de Lavras, Lavras, 2022.
Resumo: In the application of the multivariate analysis, it is necessary to follow some procedures in order not to obtain an erroneous relationship between the phenomenon of interest and the other varia- bles, that is, the model needs to be well adjusted to the characteristics of the phenomenon under study. The detection of outliers is an important method to be applied in statistical analyses, because a single outlier can cause changes in parameter estimates, also interfere with norma- lity and correlation tests between variables, in addition to alter the results of any other inference procedure. Therefore, the objective of this work is to present and compare some methods for de- tecting outliers in multivariate data. The minimum volume ellipsoid (MVE), minimum volume covariance (MCD), orthogonalized Gnanadesikan and Kettenring (OGK) methods, principal components for detection of outliers (PCOut) and Comedian were compared. To perform the comparisons, a series of simulations was used, predicting different situations using the conta- minated normal distribution. Comparisons were evaluated through the success rate (TS), which indicates the percentage of outliers that the methods correctly identified, and the false detec- tion rate (TFD), which indicates the percentage of observations that are not outliers, but were identified as outliers. It is concluded that the ideal is to use at least two methods to detect ou- tliers, since pointing out the only method as the best is a difficult task. However, the PCOut and Comedian methods obtained the best TS in most of the simulated scenarios. The comedian method obtained the best TFD.
URI: http://repositorio.ufla.br/jspui/handle/1/50190
Aparece nas coleções:Estatística e Experimentação Agropecuária - Mestrado (Dissertações)

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