| Robust bivariate boxplots and multiple outlier detection S Zani, M Riani, A Corbellini Computational Statistics & Data Analysis 28 (3), 257-270, 1998 | 126 | 1998 |
| The box–cox transformation: Review and extensions AC Atkinson, M Riani, A Corbellini | 120 | 2021 |
| Fitting Pareto II distributions on firm size: Statistical methodology and economic puzzles A Corbellini, L Crosato, P Ganugi, M Mazzoli Advances in Data Analysis: Theory and Applications to Reliability and …, 2010 | 60 | 2010 |
| The power of monitoring: how to make the most of a contaminated multivariate sample A Cerioli, M Riani, AC Atkinson, A Corbellini Statistical Methods & Applications 27, 559-587, 2018 | 56 | 2018 |
| Functional cluster analysis of financial time series A Cerioli, F Laurini, A Corbellini New Developments in Classification and Data Analysis: Proceedings of the …, 2005 | 19 | 2005 |
| Robust regression with density power divergence: Theory, comparisons, and data analysis M Riani, AC Atkinson, A Corbellini, D Perrotta Entropy 22 (4), 399, 2020 | 17 | 2020 |
| The analysis of transformations for profit-and-loss data AC Atkinson, M Riani, A Corbellini Journal of the Royal Statistical Society Series C: Applied Statistics 69 (2 …, 2020 | 15 | 2020 |
| The use of prior information in very robust regression for fraud detection M Riani, A Corbellini, AC Atkinson International Statistical Review 86 (2), 205-218, 2018 | 15 | 2018 |
| Robust Bayesian regression with the forward search: theory and data analysis AC Atkinson, A Corbellini, M Riani Test 26, 869-886, 2017 | 15 | 2017 |
| Efficient robust methods via monitoring for clustering and multivariate data analysis M Riani, AC Atkinson, A Cerioli, A Corbellini Pattern Recognition 88, 246-260, 2019 | 14 | 2019 |
| Some issues on clustering of functional data S Ingrassia, A Cerioli, A Corbellini Between Data Science and Applied Data Analysis: Proceedings of the 26 th …, 2003 | 14 | 2003 |
| Automatic robust Box–Cox and extended Yeo–Johnson transformations in regression M Riani, AC Atkinson, A Corbellini Statistical Methods & Applications 32 (1), 75-102, 2023 | 13 | 2023 |
| Rejoinder to the discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample” A Cerioli, M Riani, AC Atkinson, A Corbellini Statistical Methods & Applications 27, 661-666, 2018 | 10 | 2018 |
| Robust correspondence analysis M Riani, AC Atkinson, F Torti, A Corbellini Journal of the Royal Statistical Society Series C: Applied Statistics 71 (5 …, 2022 | 8 | 2022 |
| New methods for ordering multivariate data: an application to the performance of investment funds S Zani, M Riani, A Corbellini Applied Stochastic Models in Business and Industry 15 (4), 485-493, 1999 | 8 | 1999 |
| Robust bivariate boxplots and visualization of multivariate data M Riani, S Zani, A Corbellini Classification, Data Analysis, and Data Highways: Proceedings of the 21st …, 1998 | 7 | 1998 |
| Information criteria for outlier detection avoiding arbitrary significance levels M Riani, AC Atkinson, A Corbellini, A Farcomeni, F Laurini Econometrics and Statistics, 2022 | 6 | 2022 |
| fsdaSAS: a package for robust regression for very large datasets including the batch forward search F Torti, A Corbellini, AC Atkinson Stats 4 (2), 327-347, 2021 | 6 | 2021 |
| M. Hubert, P. Rousseeuw and P. Segaert: Multivariate functional outlier detection A Nieto-Reyes, JA Cuesta-Albertos Statistical methods & applications 24, 237-243, 2015 | 5 | 2015 |
| Labor market analysis through transformations and robust multivariate models A Corbellini, M Magnani, G Morelli Socio-Economic Planning Sciences 73, 100826, 2021 | 4 | 2021 |
