Titel: The Contribution of Young Researchers to Bayesian Statistics
Autoren/Herausgeber: Ettore Lanzarone, Francesca Ieva (Hrsg.)
Aus der Reihe: Springer Proceedings in Mathematics & Statistics
Ausgabe: Softcover reprint of the original 1st ed. 2014
Format: 23,5 x 15,5 cm
Gewicht: 349 g
Ettore Lanzarone is a Permanent Researcher at the division of Milan of the Institute of Applied Mathematics and Information Technology (IMATI) of the National Research Council of Italy (CNR), Milan, Italy. He is also Adjunct Professor Mathematical Analysis at the Politecnico di Milano, Milan, Italy. He obtained his Ph.D. in Bioengineering in June 2008 at the Politecnico di Milano, and his master degree in Biomedical Engineering cum laude in April 2004 at the Politecnico di Milano. He is member of the European Working Group on Operational Research Applied to Health Services (ORAHS) and of the Italian National Bioengineering Group (GNB). His current research interests include: parameter estimation and stochastic evolution of dynamic systems described by ordinary and partial differential equations; stochastic models for estimating the demand and planning the activities in healthcare structures; modelling and in-vitro studies of the cardiovascular fluid dynamics. Francesca Ieva is a Postdoctoral Research Fellow at the Modeling and Scientific Computing Lab (MOX), Department of Mathematics, Politecnico di Milano, Milan, Italy. She obtained her Ph.D. in Mathematical Models and Methods for Engineering and her master degree in Mathematical Engineering at the Politecnico di Milano in 2012 and 2008, respectively. She is member of ISBA (and Program Chair of the junior section), RSS, SIS (and Chair of the young section) and SIAM. Her research activities include Statistical Learning in Biomedical context, focused on modelling data arising from integration of clinical surveys and administrative databanks, Clinical Biostatistics, Healthcare assessment, Mixed Effects Models and Semi-parametric Bayesian hierarchical models, Depth Measures and Multivariate Functional Data Analysis for applications to ECG signals, Multi State Models for the analysis of chronic diseases progression like heart failures.