Andrea Cerioli
Andrea Cerioli
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Cited by
Cited by
A fuzzy approach to the measurement of poverty
A Cerioli, S Zani
Income and Wealth Distribution, Inequality and Poverty: Proceedings of the …, 1990
Exploring Multivariate Data with the Forward Search
AC Atkinson, M Riani, A Cerioli
Springer, 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
Multivariate outlier detection with high-breakdown estimators
A Cerioli
Journal of the American Statistical Association 105 (489), 147-156, 2010
Analisi dei dati e data mining per le decisioni aziendali
S Zani, A Cerioli
Giuffrè editore, 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
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
Error rates for multivariate outlier detection
A Cerioli, A Farcomeni
Computational Statistics & Data Analysis 55 (1), 544-553, 2011
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
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
Monitoring robust regression
M Riani, A Cerioli, AC Atkinson, D Perrotta
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
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
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
Modified tests of independence in 2 x 2 tables with spatial data
A Cerioli
Biometrics, 619-628, 1997
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
K-means cluster analysis and mahalanobis metrics: a problematic match or an overlooked opportunity
A Cerioli
Statistica Applicata 17 (1), 61-73, 2005
Random start forward searches with envelopes for detecting clusters in multivariate data
A Atkinson, M Riani, A Cerioli
Data Analysis, Classification and the Forward Search: Proceedings of the …, 2006
On consistency factors and efficiency of robust S-estimators
M Riani, A Cerioli, F Torti
Test 23, 356-387, 2014
Robust clustering around regression lines with high density regions
A Cerioli, D Perrotta
Advances in Data Analysis and Classification 8, 5-26, 2014
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