| Multi-state modelling of heart failure care path: A population-based investigation from Italy F Gasperoni, F Ieva, G Barbati, A Scagnetto, A Iorio, G Sinagra, ... PloS one 12 (6), e0179176, 2017 | 38 | 2017 |
| Laparoscopic retroperitoneal lymph node dissection for clinical stage I nonseminomatous germ cell tumors of the testis: safety and efficacy analyses at a high volume center N Nicolai, N Tarabelloni, F Gasperoni, M Catanzaro, S Stagni, T Torelli, ... The Journal of urology 199 (3), 741-747, 2018 | 28 | 2018 |
| Non-parametric frailty Cox models for hierarchical time-to-event data F Gasperoni, F Ieva, AM Paganoni, CH Jackson, L Sharples Biostatistics, 2018 | 27 | 2018 |
| A deep learning approach validates genetic risk factors for late toxicity after prostate cancer radiotherapy in a REQUITE multi-national cohort MC Massi, F Gasperoni, F Ieva, AM Paganoni, P Zunino, A Manzoni, ... Frontiers in oncology 10, 2020 | 22 | 2020 |
| Adherence to disease-modifying therapy in patients hospitalized for HF: findings from a community-based study M Spreafico, F Gasperoni, G Barbati, F Ieva, A Scagnetto, L Zanier, A Iorio, ... American Journal of Cardiovascular Drugs 20 (2), 179-190, 2020 | 16 | 2020 |
| Feature selection for imbalanced data with deep sparse autoencoders ensemble MC Massi, F Gasperoni, F Ieva, AM Paganoni Statistical Analysis and Data Mining: The ASA Data Science Journal 15 (3 …, 2022 | 12 | 2022 |
| Score-Driven Modeling of Spatio-Temporal Data F Gasperoni, A Luati, L Paci, E D’Innocenzo Journal of the American Statistical Association, 1-12, 2021 | 8 | 2021 |
| Evaluating the effect of healthcare providers on the clinical path of heart failure patients through a semi-Markov, multi-state model F Gasperoni, F Ieva, AM Paganoni, CH Jackson, L Sharples BMC Health Services Research 20 (1), 1-11, 2020 | 6 | 2020 |
| Robust Methods for Detecting Spontaneous Activations in fMRI Data F Gasperoni, A Luati START UP RESEARCH, 91-110, 2017 | 3 | 2017 |
| Minority class feature selection through semi-supervised deep sparse autoencoders M Massi, F Ieva, F Gasperoni, AM Paganoni Milano: Mox Report-Politecnico di Milano, 2019 | 2 | 2019 |
| Regressione lineare F Gasperoni, F Ieva, AM Paganoni Eserciziario di Statistica Inferenziale, 125-180, 2020 | 1 | 2020 |
| Eserciziario di Statistica Inferenziale F Gasperoni, F Ieva, AM Paganoni Springer Nature, 2020 | | 2020 |
| Stimatori puntuali F Gasperoni, F Ieva, AM Paganoni Eserciziario di Statistica Inferenziale, 27-43, 2020 | | 2020 |
| Test uniformemente più potente F Gasperoni, F Ieva, AM Paganoni Eserciziario di Statistica Inferenziale, 83-99, 2020 | | 2020 |
| Statistica asintotica F Gasperoni, F Ieva, AM Paganoni Eserciziario di Statistica Inferenziale, 111-122, 2020 | | 2020 |
| Statistiche sufficienti, minimali e complete F Gasperoni, F Ieva, AM Paganoni Eserciziario di Statistica Inferenziale, 19-26, 2020 | | 2020 |
| Likelihood Ratio Test F Gasperoni, F Ieva, AM Paganoni Eserciziario di Statistica Inferenziale, 63-81, 2020 | | 2020 |
| Fondamenti di probabilità e statistica F Gasperoni, F Ieva, AM Paganoni Eserciziario di Statistica Inferenziale, 3-18, 2020 | | 2020 |
| Modelli lineari generalizzati F Gasperoni, F Ieva, AM Paganoni Eserciziario di Statistica Inferenziale, 181-209, 2020 | | 2020 |
| Uniform Minimum Variance Unbiased Estimators (UMVUE) F Gasperoni, F Ieva, AM Paganoni Eserciziario di Statistica Inferenziale, 45-61, 2020 | | 2020 |
