| Clustering of financial time series in risky scenarios F Durante, R Pappadà, N Torelli Advances in Data Analysis and Classification 8 (4), 359-376, 2013 | 71 | 2013 |
| Copulas, diagonals, and tail dependence F Durante, J Fernández-Sánchez, R Pappadà Fuzzy Sets and Systems, Special issue on Aggregation functions at AGOP2013 …, 2015 | 62 | 2015 |
| Clustering of time series via non-parametric tail dependence estimation. F Durante, R Pappadà, N Torelli Statistical Papers 56 (3), 701--721, 2014 | 51 | 2014 |
| Quantification of the environmental structural risk with spoiling ties: is randomization worthwhile? R Pappadà, F Durante, G Salvadori Stochastic Environmental Research and Risk Assessment 31 (10), 2483-2497, 2017 | 32 | 2017 |
| Clustering of concurrent flood risks via Hazard Scenarios R Pappadà, F Durante, G Salvadori, C De Michele Spatial Statistics 23, 124-142, 2018 | 31 | 2018 |
| Copula–based clustering methods FML Di Lascio, F Durante, R Pappada Copulas and Dependence Models with Applications: Contributions in Honor of …, 2017 | 28 | 2017 |
| Spin-off Extreme Value and Archimedean copulas for estimating the bivariate structural risk R Pappadà, E Perrone, F Durante, G Salvadori Stochastic Environmental Research and Risk Assessment 30 (1), 327-342, 2016 | 19 | 2016 |
| Relabelling in Bayesian mixture models by pivotal units L Egidi, R Pappada, F Pauli, N Torelli Statistics and Computing 28 (4), 957-969, 2018 | 18 | 2018 |
| Cluster analysis of time series via Kendall distribution. F Durante, R Pappadà Strengthening Links Between Data Analysis and Soft Computing, Advances in …, 2015 | 11 | 2015 |
| A portfolio diversification strategy via tail dependence clustering H Wang, R Pappadà, F Durante, E Foscolo Soft Methods for Data Science 456, 511-518, 2017 | 10 | 2017 |
| A spatially‐weighted AMH copula‐based dissimilarity measure for clustering variables: An application to urban thermal efficiency FML Di Lascio, A Menapace, R Pappadà Environmetrics 35 (1), e2828, 2024 | 6 | 2024 |
| An approach to cluster time series extremes with spatial constraints A Benevento, F Durante, R Pappada SEAS IN. Book of the Short Papers, 679-684, 2023 | 6 | 2023 |
| Discrimination in machine learning algorithms R Pappadà, F Pauli Book of Short Papers SIS 2018, 2018 | 6* | 2018 |
| pivmet: Pivotal methods for Bayesian relabelling and k-means clustering L Egidi, R Pappadà, F Pauli, N Torelli arXiv preprint arXiv:2103.16948, 2021 | 4 | 2021 |
| K-means seeding via MUS algorithm L Egidi, R Pappadà, F Pauli, N Torelli Book of Short Papers SIS 2018, 2018 | 4 | 2018 |
| Maxima Units Search (MUS) algorithm: methodology and applications L Egidi, R Pappadà, N Torelli, F Pauli Studies in Theoretical and Applied Statistics, 2018 | 4 | 2018 |
| A Graphical Tool for Copula Selection Based on Tail Dependence R Pappadà, F Durante, N Torelli Classification,(Big) Data Analysis and Statistical Learning, 211-218, 2018 | 3 | 2018 |
| Tail-dependence clustering of time series with spatial constraints A Benevento, F Durante, R Pappadà Environmental and Ecological Statistics 31 (3), 801-817, 2024 | 2 | 2024 |
| Clustering of financial time series in extreme scenarios F Durante, R Pappadà Atti della XLVI Riunione Scienti ca della Societ a Italiana di Statistica …, 2012 | 2 | 2012 |
| Cluster analysis and conditional copula: a joint approach to analyse energy demand FML Di Lascio, R Pappada CLADAG 2023. Book of abstracts and short papers, 427-430, 2023 | 1 | 2023 |
Fabrizio DuranteDipartimento di Matematica e Fisica "Ennio De Giorgi", Università del SalentoE-mail megerősítve itt: unisalento.it
