Statistics @ MOX


 SEMINAR NOTICE: “Nonparametric frailty Cox model for grouped time-to-event data”

Speaker:  Francesca Gasperoni
Affiliation:  MOX laboratory, Politecnico di Milano
When:  Friday 23th June, at 11:30am
Where:  Aula Saleri – 6th floor – Department of Mathematics – Politecnico di Milano
Abstract:
In this work, we propose an innovative model for fitting grouped survival data and for detecting a second level of clusters among groups. In order to achieve this goal, we start from a classical semiparametric Cox model and we add a nonparametric discrete random term as a multiplicative factor. This research question arose from a project about healthcare management of Regione Lombardia. We analyze a rich administrative database, where several information about patients is collected (i.e. dates of hospitalizations, death, comorbidities, procedures etc.). In this framework, patients are the statistical units and hospitals are the known groups. Through the application of this new model, we are able to detect hidden populations among hospitals and we provide a clustering tool for survival data.

Contact: laura.sangalli@polimi.it


 SEMINAR NOTICE: “Student and School Performance in the OECD: a Machine Learning Approach”

Speaker:  Chiara Masci
Affiliation:  MOX laboratory, Politecnico di Milano
When:  Friday 23th June, at 12:00pm
Where:  Aula Saleri – 6th floor – Department of Mathematics – Politecnico di Milano
Abstract:
In this work, we develop and apply novel machine learning and statistical methods to analyse the determinants of students’ PISA 2015 test scores in nine countries: Australia, Canada, France, Germany, Italy, Japan, Spain, UK and USA. The aim is to find out which student characteristics are associated with test scores and which school characteristics are associated to school value-added (measured at school level). A specific aim of our approach is to explore non-linearities in the associations between covariates and test scores, as well as to model interactions between school-level factors in affecting results. In order to address these issues, we apply a two-stage methodology using flexible tree-based methods. We first run multilevel regression trees in the first stage, to estimate school value-added. In the second stage, we relate the estimated school value-added to school level variables by means of regression trees and boosting. Results show that while several student and school level characteristics are significantly associated to students’ achievements, there are marked differences across countries. The proposed approach allows an improved description of the structurally different educational production functions across countries.

Contact: laura.sangalli@polimi.it


 SEMINAR NOTICE: “Imaging the brain microstructure with diffusion-weighted mri”

Speaker:  Maxime Taquet
Affiliation:  University of Louvain and Harvard Medical School
When:  Monday 26th June, at 2:00pm
Where:  Aula consiglio – 7th floor – Department of Mathematics – Politecnico di Milano
Abstract:
The brain microstructure is the complex organization of axons, neurons and other cells that support the neural functions. Its mapping at the whole-brain level holds promise to the identification and characterization of neurological and psychiatric disorders as well as the assessment of response to treatment. Diffusion-weighted imaging has been at the forefront of developments of brain microstructure imaging. It is based on the recording of movements of pools of water molecules as they hit the cellular barriers in the brain. In this talk, I will provide a gentle introduction to DWI and microstructure imaging, present some recent developments and outline exciting challenges that the field is currently facing.

Contact: simone.vantini@polimi.it