Procedimento para identificar outliers por meio da distribuição acumulada de mínimo em um modelo com resposta gama

• Ano de defesa: 2016
• Autor(a) principal: Resende, Mariana
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Lavras; Programa de Pós-Graduação em Estatística e Experimentação Agropecuária; UFLA; brasil; Departamento de Ciências Exatas
• Palavras-chave em Português: Simulação Monte Carlo; Distância de Mahalanobis; Taxa de mistura; Dispersãoparâmetro de dispersão; GLM; Monte Carlo Simulation; Mahalanobis distance; Mixing rate; Dispersion parameter; Probabilidade e Estatística Aplicadas
• Link de acesso: http://repositorio.ufla.br/jspui/handle/1/11012

This study aimed to propose a procedure based on the accumulated distribution of minimums to identify generalized outlier models by using Gamma response. To validate this methodology, we used Monte Carlo simulation, considering the scenarios defined by the combination of different sample sizes, the contamination rate and the distributions with different degrees of asymmetry. In this context, probabilities related to classification and accuracy errors were obtained from 500 Monte Carlo achievements. We concluded that the method is effective for presenting high accuracy probability. In terms of implementation, through the illustrated example, given the similarity between the new proposed approach, compared to approaches based by the lever matrix and Cook’s distance, we conclude that the procedure suggested in this study is feasible for implementation in response to Gamma distribution.