Project

Discipline

Small area estimation; Multilevel models; ANOVA; Growth curve models; Estimation and testing; Clustered data; time series; repeated measurements; medical data; biostatistics; data contamination

Lead
Lay summary
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.

Direct link to Lay Summary

Last update: 21.02.2013

Active participation

Title	Year

Mixed linear models (MLM) are flexible models for the analysis of complexdesigns data made across many disciplines of the social sciences. It is therefore important to develop statistical tools to estimate and test thesemodels. MLM are used whenever several measurements are made on one particular unit or subject or cluster, or even nested clusters. Their particularity, which makes them also more difficult to understand and analyze, is that they include random effects that capture the variability of the clusters. Although the literature has already provided a great amount of statistical procedures, there are some important aspects that still need to be developed. In particular, more or less automatic model selection procedures, especially for the number and nature of the random effects to introduce in the model, as well a correct response prediction when the normality assumption underlying MLM is not fully respected by the data at hand, are needed.The aim of this research project is mainly to develop statistical methods todeal with these issues. 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, we will also provide new functions in the statistical software R.