Resumen inglés |
Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as
departures from error assumptions and the presence of outliers and influential observations with the
fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis
assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian
approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting
algorithms steps to obtain the posterior summaries of interest. Some influence methods, such as
the local influence, total local influence of an individual, local influence on predictions and generalized
leverage were derived, analyzed and discussed in survival data with a cure fraction and covariates.
The relevance of the approach was illustrated with a real data set, where it is shown that, by removing
the most influential observations, the decision about which model best fits the data is changed. |