Greco, L., Menardi, G. (2025) Robust mean-shift clustering based on impartial trimming. Statistics and Computing, 35(188).
Ferrari, M., Brazzale, A.R., Menardi, G. (2025) Trajectory Reconstruction in Muon Scattering Tomography Using Two-Component Mixture Modelling. In: Supervised and unsupervised Statistical data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. D’Ambrosio, de Rooij, De Roover, Iorio, La Rocca Editors. Springer. ISSN 1431-8814.
Montin, A., Brazzale, A. R., Menardi, G., Sottosanti, A. (2024) Locating γ-ray sources on the celestial sphere via modal clustering. Statistical Methods & Applications, 33, 153–172.
Ferraccioli, F., Menardi, G. (2023) Modal clustering of matrix-variate data. Advances in Data Analysis and Classification, 17(2).
Muracchioli, M, Menardi, G., D’Agostini, M., Franchin, G., Colombo, P (2023) Modeling the compressive strength of metakaolin-based geopolymers based on the statistical analysis of experimental data. Applied Clay Science, 242.
Menardi, G., De Stefano, D. (2022). Density-based clustering of social networks, Journal of the Royal Statistical Society, Series A -Statistics in Society, 185(3).
Casa, A., Menardi, G. (2022). Nonparametric semi-supervised classification with application to signal detection in high energy physics. Statistical methods and Applications, 31(3)
Stakia, A., Dorigo, T., Banelli, G., Bortoletto, D., Casa, A., de Castro, P., Delaere, C., Donini, J., Finos, L., Gallinaro, M., Giammanco, A., Held, A., Morales, F. J., Kotkowski, G., Liew, S. P., Maltoni, F., Menardi, G., Papavergou, I., Saggio, A., Scarpa, B., Strong, G. C., Tosciri, C., Varela, J., Vischia, P., Weiler, A. (2021). Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider. Reviews in Physics, 7
Casa, A. Scrucca, L. Menardi, G. (2021). Better than the best? Answers via model ensemble in density-based clustering. Advances in Data Analysis and Classification, 15(3).Casa, A., Bouveyron, C., Erosheva, E., Menardi, G. (2021). Co-clustering of Time-Dependent Data via the Shape Invariant Model. Journal of Classification, 38(3).
Costantin, D., Menardi, G., Brazzale, A. R., Bastieri, D., Fan, J. H. (2020). A novel approach for pre-filtering event sources using the von Mises–Fisher distribution.Astrophysics and Space Science. 365(3).
Lunardon, N., Menardi, G. (2020) Comment on “Wang et al. (2005), Robust Estimating Functions and Bias Correction for Longitudinal Data Analysis”. Biometrics, 76(3).
Casa, A., Chacón, J.E., Menardi, G. (2020), Modal clustering asymptotics with applications to bandwidth selection. Electronic Journal of Statistics 14(1)
Menardi, G. (2020). Nonparametric clustering for image segmentation. Journal of Statistical Analsyis and Data Mining. 13(1)
Menardi, G. (2016) A review on modal clustering. International Statistical review. 84(3), 413-433.
Azzalini, A., Menardi, G. (2016) Density-based clustering with non-continuous data. Computational Statistics. 31(2), 771-798.
Menardi, G., Lisi, F. (2015) Double clustering for rating mutual funds. Electronic Journal of Applied Statistical Analysis. 8(1), 44-56.
Lunardon, N. Menardi, G. Torelli, N. (2014) ROSE: a package for class imbalance learning. The R Journal, 6(1): 79-89.
Menardi, G., Azzalini, A. (2014) An advancement in clustering via nonparametric density estimation. Statistics and Computing, 24(5), 753-767.
Azzalini, A., Menardi, G. (2014) Clustering via nonparametric density estimation: the R package pdfcluster, Journal of Statistical Software, 57(11), 1-26.
Menardi, G., Torelli, N. (2014). Training and assessing classification rules with imbalanced data, Data Mining and Knowledge Discovery. 28(1), 92-122.
Menardi, G. Torelli, N. (2013) Effect of training set selection when predicting defaulting small and medium sized enterprises with unbalanced data. Journal of Credit Risk 9(4).
Menardi, G., Torelli, N. (2013) Reducing data dimension for cluster detection, Journal of Statistical Computation and Simulation. 83(11), 2047-2063.
Menardi, G. (2013). Multidimensional connected set detection in clustering based on nonparametric density estimation. In: M. Grigoletto, F. Lisi, S. Petrone: Complex Models and Computational Methods in Statistics. Springer/Physica Verlag, Heidelberg.
Menardi, G., Lisi, F. (2012) Are performance measures equally stable?, Annals of Finance, 8(4), 553-570.
Menardi, G., Lisi, F. (2012) On the stability of performance measures over time: an empirical study. The Journal of Performance Measurement. winter 2011/2012: 36-45
Menardi, G. (2011) Density based Silhouette diagnostics for clustering methods, Statistics and Computing, 21(3), 295-308.
Menardi, G., Tedeschi, F., Torelli, N. (2011) On the use of boosting procedures to predict the risk of default, In Fichet, B., Piccolo, D., Verde, R., Vichi, M. (Eds.) Classification and Multivariate Analysis for Complex Data Structures, Studies in Classification, Data Analysis, and Knowledge Organization, Springer. ISBN: 978-3-642-13311-4.
Menardi, G. Torelli, N. (2010) Preserving the clustering structure by a projection pursuit approach. Palumbo, F., Lauro, C.N. and Greenacre, M. J. Data Analysis and Classification, serie: Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Berlin. ISBN: 978-3-642-03738-2.
Menardi, G., Monte, A., Pauli, F. (2006) Stima delle Forze di lavoro per le province del Friuli Venezia Giulia integrando rilevazioni campionarie e fonti amministrative. In Liseo, B., Montanari, G. E., Torelli, N.,Metodi Statistici per l’integrazione di dati da fonti diverse, Franco Angeli editore. ISBN 88-464-6990-9.