Análise de diagnóstico em modelos de regressão ZAGA e ZAIG

• Ano de defesa: 2016
• Autor(a) principal: Rodrigues, Juliana Scudilio
• Orientador(a): Pereira, Gustavo Henrique de Araujo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs
• Palavras-chave em Português: Análise de diagnóstico; Fundo de investimento; Modelos de regressão in acionado no zero; Modelo ZAGA; Modelo ZAIG; Resíduo quantílico
• Palavras-chave em Inglês: Diagnostic analysis; In ated regression models; Investiment funds; Quantile residual; ZAGA models; ZAIG models
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/7857

Residuals play an important role in checking model adequacy and in the identi cation of outliers and in uential observations. In this paper, we studied two class of residuals for the zero adjusted gamma regression model (ZAGA) and the zero adjusted inverse Gaussian regression model (ZAIG). These classes of residuals are function of a residual for the continuous component of the model and the maximum likelihood estimate of the probability of the observation assuming the zero value. Monte Carlo simulation studies are performed to examine the properties of this class of residuals in both models (ZAGA and ZAIG). Results showed that a residual of one of these class has some similar properties to the standard normal distribution in the studied models. We also described ZAGA and ZAIG regression models, studied properties of some residuals in generalized linear models with response gamma and inverse Gaussian and discussed other aspects of diagnostic analysis in ZAGA and ZAIG models. To nsih, we presented a real dataset application from investment funds of Brazil. We tted the ZAIG model to illustrate the topics discussed and showed the importance of these models and the advantages of one of the studied residuals in the analysis of real dataset.