Small area estimation; Multilevel models; ANOVA; Growth curve models; Estimation and testing; Clustered data; time series; repeated measurements; medical data; biostatistics; data contamination
Stebler Yannick, Guerrier Stéphane, Skaloud Jan, Victoria-Feser Maria-Pia (2012), A Framework for Inertial Sensor Calibration Using Complex Stochastic Error Models, in Proceedings of IEEE/ ION PLANS 2012
, Myrtle Beach, SC, USA.
Guerrier S., Waegli Adrian, Skalud J., Victoria-Feser M.-P. (2012), Fault Detection and Isolation in Multiple MEMS-IMUs Configurations, in IEEE Transactions on Aerospace and Electronic Systems
, 48, 2015-2031.
Stebler Yannick, Guerrier Stéphane, Skalud Jan, Victoria-Feser Maria-Pia (2011), Constrained Expectation-Maximization Algorithm for Stochastic Inertial Error Modeling: Study of Feasibility, in Measurement Science and Technology
, 22(8), 1-12.
Stebler Yannick, Guerrier Stéphane, Skaloud Jan, Victoria-Feser Maria-Pia (2011), Improving Modeling of MEMS-IMUs Operating in GNSS-denied Conditions, in Proceedings of the ION GNSS 2011
, Portland, OR, USA.
Guerrier Stéphane, Molinari Roberto, Skalud Jan, Victoria-Feser Maria-Pia, An Algorithm for Automatic Inertial Sensors Calibration, in Proceedings of the ION GNSS 2013
, Nashville, TN, USA.
Dupuis D. J, Victoria-Feser M.-P., Robust VIF Regression with Application to Variable Selection in Large Datasets, in Annals of Applied Statistics
Guerrier Stéphane, Stebler Yannick, Skaloud Jan, Victoria-Feser Maria-Pia, Wavelet variance based estimation for composite stochastic processes, in Journal of the American Statistical Association
, 108(503), 1021-1030.
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.