Chiara Masci

Assistant Professor

Contact Information

Phone:
+39 02 2399 4513
Fax:
+39 02 2399
Office:
14-Nave
Email:

Keywords

Statistical learning
Health Analytics

Publications

 

Available MOX Reports

BERGONZOLI, G.; ROSSI, L.; MASCI, C.
Ordinal Mixed-Effects Random Forest
MASCI, C.; SPREAFICO, M.; IEVA, F.
Joint modelling of recurrent and terminal events with discretely-distributed non-parametric frailty: application on re-hospitalizations and death in heart failure patients
RAGNI, A.; IPPOLITO, D.; MASCI, C.
Assessing the Impact of Hybrid Teaching on Students' Academic Performance via Multilevel Propensity Score-based techniques
BERTOLETTI, A.; CANNISTRà, M.; DIAZ LEMA, M.; MASCI, C.; MERGONI, A.; ROSSI, L.; SONCIN, M.
The Determinants of Mathematics Achievement: A Gender Perspective Using Multilevel Random Forest
RAGNI, A.; MASCI, C.; IEVA, F.; PAGANONI, A. M.
Clustering Hierarchies via a Semi-Parametric Generalized Linear Mixed Model: a statistical significance-based approach
MASCI, C.; CANNISTRà, M.; MUSSIDA, P.
Modelling time-to-dropout via Shared Frailty Cox Models. A trade-off between accurate and early predictions
LURANI CERNUSCHI , A.; MASCI, C.; CORSO, F.; MUCCINI, C.; CECCARELLI, D.; SAN RAFFAELE HOSPITAL GALLI, L.; IEVA, F.; PAGANONI, A.M.; CASTAGNA, A.
A neural network approach to survival analysis for modelling time to cardiovascular diseases in HIV patients with longitudinal observations
MASCI, C.; IEVA, F.; PAGANONI, A.M.
A multinomial mixed-effects model with discrete random effects for modelling dependence across response categories
MASCI, C.; IEVA, F.; PAGANONI A.M.
EM algorithm for semiparametric multinomial mixed-effects models
CANNISTRà,M.; MASCI, C.; IEVA, F.; AGASISTI, T.; PAGANONI, A.M.
Not the magic algorithm: modelling and early-predicting students dropout through machine learning and multilevel approach
PELLAGATTI, M.; MASCI, C.; IEVA, F.; PAGANONI A.M.
Generalized Mixed-Effects Random Forest: a flexible approach to predict university student dropout
MASCI, C.; IEVA, F.; AGASISTI, T.; PAGANONI A.M.
Evaluating class and school effects on the joint achievements in different subjects: a bivariate semi-parametric mixed-effects model
FONTANA, L.; MASCI, C.; IEVA, F.; PAGANONI, A.M.
Performing Learning Analytics via Generalized Mixed-Effects Trees
MASCI, C.; PAGANONI, A.M.; IEVA, F.
Non-parametric mixed-effects models for unsupervised classification of Italian schools
SCHILTZ, F.; MASCI, C.; AGASISTI, T.; HORN, D.
Using Machine Learning to Model Interaction Effects in Education: a Graphical Approach.
MASCI, C.; JOHNES, G.; AGASISTI, T.
Student and School Performance in the OECD: a Machine Learning Approach.
AGASISTI,T.; IEVA, F.; MASCI, C.; PAGANONI, A.M.
Does class matter more than school? Evidence from a multilevel statistical analysis on Italian junior secondary school students
MASCI, C,; IEVA, F.; AGASISTI, T.; PAGANONI, A.M.
Bivariate multilevel models for the analysis of mathematics and reading pupils' achievements