Iterative proportional scaling based on a robust start estimator

Model selection procedures in graphical modeling are essentially based on the estimation of covariance matrices under conditional independence restrictions. Such model selection procedures can react heavily on the presence of outlying observations. One reason for this might be that the covariance estimation is influenced by outliers. Hence, a robust procedure to estimate a covariance matrix under conditional independence restrictions is needed. As a first step to robustify the model building process in graphical modeling we propose to use a modified iterative proportional scaling algorithm, starting with a robust covariance estimator.