MASCI, CHIARA
MASCI, CHIARA
Dipartimento di Economia, Management e Metodi Quantitativi
A statistical significance-based approach for clustering grouped data via generalized linear model with discrete random effects
2025 A. Ragni, C. Masci, F. Ieva, A.M. Paganoni
Analysis of Higher Education Dropouts Dynamics Through Functional Decomposition of Recurrent Events on Time-to-Event Processes
2024 A. Ragni, C. Masci, A.M. Paganoni
Inferential tools for assessing dependence across response categories in multinomial models with discrete random effects
2024 C. Masci, F. Ieva, A.M. Paganoni
Assessing the impact of hybrid teaching on students’ academic performance via multilevel propensity score-based techniques
2024 A. Ragni, D. Ippolito, C. Masci
Modelling time-to-dropout via shared frailty Cox models. A trade-off between accurate and early predictions
2024 C. Masci, M. Cannistrà, P. Mussida
The determinants of mathematics achievement: A gender perspective using multilevel random forest
2023 A. Bertoletti, M. Cannistrà, M. Diaz Lema, C. Masci, A. Mergoni, L. Rossi, M. Soncin
Semi-parametric generalized linear mixed effects models for binary response for the analysis of heart failure hospitalizations = Modelli lineari generalizzati ad effetti misti semi-parametrici per risposta binaria per l’analisi di ospedalizzazioni per scompenso cardiaco
2022 A. Ragni, C. Masci, F. Ieva, A.M. Paganoni
Modelling time to university dropout by means of time-dependent frailty COX PH models = Modelli di COX tempo-dipendenti con frailty per la modellizzazione del tempo all’abbandono universitario
2022 M. Giovio, P. Mussida, C. Masci
Multinomial Multilevel Models with Discrete Random Effects: a Multivariate Clustering Tool
2022 C. Masci, F. Ieva, A.M. Paganoni
Early-predicting dropout of university students: an application of innovative multilevel machine learning and statistical techniques
2022 M. Cannistrà, C. Masci, F. Ieva, T. Agasisti, A. Maria Paganoni
PET/CT‑based radiomics of mass‑forming intrahepatic cholangiocarcinoma improves prediction of pathology data and survival
2022 F. Fiz, C. Masci, G. Costa, M. Sollini, A. Chiti, F. Ieva, G. Torzilli, L. Viganò
Semiparametric multinomial mixed-effects models: a University student profiling tool
2022 C. Masci, F. Ieva, A.M. Paganoni
Multinomial semiparametric mixed-effects model for profiling engineering university students = Modello multinomiale a effetti misti semiparametrico per la profilazione di studenti universitari di ingegneria
2021 C. Masci, F. Ieva, A.M. Paganoni
Virtual biopsy in action: a radiomic-based model for CALI prediction = Biopsia virtuale basata su analisi radiomica per la previsione di CALI
2021 F. Ieva, G. Baroni, L. Cavinato, C. Masci, G. Costa, F. Fiz, A. Chiti, L. Viganò
Virtual biopsy for diagnosis of chemotherapy-associated liver injuries and steatohepatitis: a combined radiomic and clinical model in patients with colorectal liver metastases
2021 G. Costa, L. Cavinato, C. Masci, F. Fiz, M. Sollini, L.S. Politi, A. Chiti, L. Balzarini, A. Aghemo, L. di Tommaso, F. Ieva, G. Torzilli, L. Viganò
Performing learning analytics via generalised mixed-effects trees
2021 L. Fontana, C. Masci, F. Ieva, A.M. Paganoni
Evaluating class and school effects on the joint student achievements in different subjects: a bivariate semiparametric model with random coefficients
2021 C. Masci, F. Ieva, T. Agasisti, A.M. Paganoni
Generalized mixed-effects random forest: A flexible approach to predict university student dropout
2021 M. Pellagatti, C. Masci, F. Ieva, A.M. Paganoni
Generalized Mixed Effects Random Forest: does Machine Learning help in predicting university student dropout? = Random forest generalizzati a effetti misti: il machine learning aiuta a prevedere l’abbandono degli studenti universitari?
2020 M. Pellagatti, C. Masci, F. Ieva, A.M. Paganoni
Bivariate semi-parametric mixed-effects models for classifying the effects of Italian classes on multiple student achievements
2019 C. Masci, F. Ieva, T. Agasisti, A.M. Paganoni