| A fuzzy approach to the measurement of poverty A Cerioli, S Zani Income and Wealth Distribution, Inequality and Poverty: Proceedings of the …, 1990 | 848 | 1990 |
| Exploring Multivariate Data with the Forward Search AC Atkinson, M Riani, A Cerioli Springer, 2004 | 312 | 2004 |
| Finding an unknown number of multivariate outliers M Riani, AC Atkinson, A Cerioli Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2009 | 221 | 2009 |
| Multivariate outlier detection with high-breakdown estimators A Cerioli Journal of the American Statistical Association 105 (489), 147-156, 2010 | 156 | 2010 |
| Analisi dei dati e data mining per le decisioni aziendali S Zani, A Cerioli Giuffrè editore, 2007 | 118 | 2007 |
| The forward search: Theory and data analysis AC Atkinson, M Riani, A Cerioli Journal of the korean statistical society 39 (2), 117-134, 2010 | 116 | 2010 |
| The ordering of spatial data and the detection of multiple outliers A Cerioli, M Riani Journal of computational and graphical statistics 8 (2), 239-258, 1999 | 69 | 1999 |
| Newcomb–Benford law and the detection of frauds in international trade A Cerioli, L Barabesi, A Cerasa, M Menegatti, D Perrotta Proceedings of the National Academy of Sciences 116 (1), 106-115, 2019 | 62 | 2019 |
| Error rates for multivariate outlier detection A Cerioli, A Farcomeni Computational Statistics & Data Analysis 55 (1), 544-553, 2011 | 62 | 2011 |
| 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 |
| Monitoring robust regression M Riani, A Cerioli, AC Atkinson, D Perrotta | 51 | 2014 |
| Controlling the size of multivariate outlier tests with the MCD estimator of scatter A Cerioli, M Riani, AC Atkinson Statistics and Computing 19, 341-353, 2009 | 47 | 2009 |
| Modified tests of independence in 2 x 2 tables with spatial data A Cerioli Biometrics, 619-628, 1997 | 42 | 1997 |
| Goodness-of-fit testing for the Newcomb-Benford law with application to the detection of customs fraud L Barabesi, A Cerasa, A Cerioli, D Perrotta Journal of Business & Economic Statistics 36 (2), 346-358, 2018 | 41 | 2018 |
| Strong consistency and robustness of the forward search estimator of multivariate location and scatter A Cerioli, A Farcomeni, M Riani Journal of Multivariate Analysis 126, 167-183, 2014 | 41 | 2014 |
| Finding the number of normal groups in model-based clustering via constrained likelihoods A Cerioli, LA García-Escudero, A Mayo-Iscar, M Riani Journal of Computational and Graphical Statistics 27 (2), 404-416, 2018 | 39 | 2018 |
| K-means cluster analysis and Mahalanobis metrics: a problematic match or an overlooked opportunity? A Cerioli Statistica Applicata 17, 61-73, 2005 | 35 | 2005 |
| On consistency factors and efficiency of robust S-estimators M Riani, A Cerioli, F Torti Test 23, 356-387, 2014 | 31 | 2014 |
| Robust clustering around regression lines with high density regions A Cerioli, D Perrotta Advances in Data Analysis and Classification 8, 5-26, 2014 | 31 | 2014 |
| rA fuzzy approach to the measurement of poverty, s in Income and Wealth Distribution, Inequality and Poverty, ed. by C A Cerioli, S Zani Dagum, and M. Zenga, pp. 272t284. Springer&Verlag, Berlin, 1990 | 31 | 1990 |
