Statistics plays an important role in the research process. To verify a theoretical statement on a given problem, experiments are set and observations made on a sample of subjects and the collected information is then analyzed through the use of statistical models and tools. One can then make inference from the sample to the unobservable population. The results of the research rely therefore heavily on the quality of the statistical analysis. Modern research in the life sciences imply the use of more and more complex models such as mixed linear models (MLM). These models allow the analysis of multivariate or clustered data, i.e. several measurements per sampled subject. They are used in many fields such as in biology, medicine, psychology, education, sociology, political sciences, economics, business. A MLM can be used for example to understand the mechanisms that make individuals choose among different media (radio, television), or to react to different stimuli or drugs in a medical setting. New or improved statistical methods are necessary because researchers become more and more aware of the potential biases induced in the results of the analysis by either the use of too simplistic statistical models and/or data contamination. In this project we will develop more or less automatic model selection procedures, which are usually based on the optimization of response prediction criteria, as well as propose correct (robust) response predictions when the distributional assumptions underlying MLM is not fully respected by the data at hand. We also aim at applying the new development for the analysis of real data sets in the social, economics and psychological sciences and, in order to make this transfer of technology as efficient as possible, these methods will also be implemented in statistical packages for their availability to the community of researchers.