The Log-gamma-logistic Regression Model: Estimation, Sensibility and Residual Analysis - Elizabeth M. Hashimoto; Edwin M.M. Ortega; Gauss M. Cordeiro; G.G. Hamedani (Journal of Statistical Theory and Applications (JSTA), 2017)
In this paper, we formulate and develop a log-linear model using a new distribution called the log-gammalogistic. We show that the new regression model can be applied to censored data since it represents a parametric family of models that includes as sub-models several widely-known regression models and therefore can be used more effectively in the analysis of survival data. We obtain maximum likelihood estimates of the model parameters by considering censored data and evaluate local influence on the estimates of the parameters by taking different perturbation schemes. Some global-influence measurements are also investigated. Further, for different parameter settings, sample sizes and censoring percentages, various simulations are performed. In addition, the empirical distributions of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to modified deviance residuals in the proposed regression model applied to censored data. We demonstrate that our extended regression model is very useful to the analysis of real data and may give more realistic fits than other special regression models.