| Comparing methods for comparing networks M Tantardini, F Ieva, L Tajoli, C Piccardi Scientific reports 9 (1), 17557, 2019 | 282 | 2019 |
| Statistical challenges of administrative and transaction data DJ Hand Journal of the Royal Statistical Society Series A: Statistics in Society 181 …, 2018 | 183 | 2018 |
| Multivariate functional clustering for the morphological analysis of electrocardiograph curves F Ieva, AM Paganoni, D Pigoli, V Vitelli Journal of the Royal Statistical Society Series C: Applied Statistics 62 (3 …, 2013 | 117 | 2013 |
| Multi-state modelling of repeated hospitalisation and death in patients with heart failure: the use of large administrative databases in clinical epidemiology F Ieva, CH Jackson, LD Sharples Statistical methods in medical research 26 (3), 1350-1372, 2017 | 92 | 2017 |
| Depth measures for multivariate functional data F Ieva, AM Paganoni Communications in Statistics-Theory and Methods 42 (7), 1265-1276, 2013 | 82 | 2013 |
| Non-invasive optical estimate of tissue composition to differentiate malignant from benign breast lesions: A pilot study P Taroni, AM Paganoni, F Ieva, A Pifferi, G Quarto, F Abbate, E Cassano, ... Scientific reports 7 (1), 40683, 2017 | 80 | 2017 |
| Methodological issues on the use of administrative data in healthcare research: the case of heart failure hospitalizations in Lombardy region, 2000 to 2012 C Mazzali, AM Paganoni, F Ieva, C Masella, M Maistrello, O Agostoni, ... BMC Health Services Research 16, 1-10, 2016 | 79 | 2016 |
| Statistical medical fraud assessment: exposition to an emerging field T Ekin, F Ieva, F Ruggeri, R Soyer International Statistical Review 86 (3), 379-402, 2018 | 66 | 2018 |
| Generalized mixed‐effects random forest: A flexible approach to predict university student dropout M Pellagatti, C Masci, F Ieva, AM Paganoni Statistical Analysis and Data Mining: The ASA Data Science Journal 14 (3 …, 2021 | 65 | 2021 |
| PET/CT-based radiomics of mass-forming intrahepatic cholangiocarcinoma improves prediction of pathology data and survival F Fiz, C Masci, G Costa, M Sollini, A Chiti, F Ieva, G Torzilli, L Viganò European journal of nuclear medicine and molecular imaging 49 (10), 3387-3400, 2022 | 53 | 2022 |
| Optical identification of subjects at high risk for developing breast cancer P Taroni, G Quarto, A Pifferi, F Ieva, AM Paganoni, F Abbate, N Balestreri, ... Journal of biomedical optics 18 (6), 060507-060507, 2013 | 46 | 2013 |
| A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data A Martino, A Ghiglietti, F Ieva, AM Paganoni Statistical Methods & Applications 28, 301-322, 2019 | 43 | 2019 |
| 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 | 40 | 2017 |
| Data mining application to healthcare fraud detection: a two-step unsupervised clustering method for outlier detection with administrative databases MC Massi, F Ieva, E Lettieri BMC medical informatics and decision making 20, 1-11, 2020 | 38 | 2020 |
| Detecting and visualizing outliers in provider profiling via funnel plots and mixed effect models F Ieva, AM Paganoni Health care management science 18, 166-172, 2015 | 37 | 2015 |
| Early-predicting dropout of university students: an application of innovative multilevel machine learning and statistical techniques M Cannistrà, C Masci, F Ieva, T Agasisti, AM Paganoni Studies in Higher Education 47 (9), 1935-1956, 2022 | 36 | 2022 |
| Non-parametric frailty Cox models for hierarchical time-to-event data F Gasperoni, F Ieva, AM Paganoni, CH Jackson, L Sharples Biostatistics 21 (3), 531-544, 2020 | 36 | 2020 |
| Performance assessment using mixed effects models: a case study on coronary patient care N Grieco, F Ieva, AM Paganoni IMA Journal of Management Mathematics 23 (2), 117-131, 2012 | 34 | 2012 |
| Multivariate functional clustering for the analysis of ECG curves morphology F Ieva, A Paganoni, D Pigoli, V Vitelli | 33 | 2011 |
| 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, 541281, 2020 | 30 | 2020 |
Anna Maria PaganoniProfessore di Statistica, Politecnico di Milano, ItalyVerified email at polimi.it
