gms | German Medical Science

The generalized linear model (GLM) allows the distribution of the dependent variable to belong to the exponential family, which also includes not continuous distributions. Other than simple linear relationships between response and explanatory variables are permitted. The robust approach to statistical modeling aims at deriving methods that produce reliable estimates not only when data follow a given distribution exactly, but also when this happens approximately. We have explored the current facilities of the free software environment for statistical computing R to identify influential cases in GLMs and to carry out robust GLMs. We summarize relevant theoretical and technical details, and apply R to investigate several real datasets by robust and standard GLMs, including robust variance estimates. Methods and software for robust estimation of GLM are still sparse and mainly limited to Logistic and Poisson regression. The “car” and “robustbase” packages provide convenient functions for diagnostic plots and robust GLMs. In many practical situations, the implementation of robust GLMs is relatively straightforward. During the presentation, we will illustrate the benefit of comparing standard and robust GLM estimates by using real examples.