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Roberta Pappadà
Roberta Pappadà
Department of Economics, Business, Mathematics and Statistics (DEAMS) University of Trieste
Verified email at units.it - Homepage
Title
Cited by
Cited by
Year
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
652013
Copulas, diagonals, and tail dependence
F Durante, J Fernández-Sánchez, R Pappadà
Fuzzy Sets and Systems, Special issue on Aggregation functions at AGOP2013 …, 2015
552015
Clustering of time series via non-parametric tail dependence estimation.
F Durante, R Pappadà, N Torelli
Statistical Papers 56 (3), 701--721, 2014
462014
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
312017
Copula–based clustering methods
FML Di Lascio, F Durante, R Pappadà
Copulas and Dependence Models with Applications: Contributions in Honor of …, 2017
252017
Clustering of concurrent flood risks via Hazard Scenarios
R Pappadà, F Durante, G Salvadori, C De Michele
Spatial Statistics 23, 124-142, 2018
242018
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
192016
Relabelling in Bayesian mixture models by pivotal units
L Egidi, R Pappada, F Pauli, N Torelli
Statistics and Computing 28 (4), 957-969, 2018
172018
Cluster analysis of time series via Kendall distribution.
F Durante, R Pappadà
Strengthening Links Between Data Analysis and Soft Computing, Advances in …, 2015
102015
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
92017
Maxima Units Search (MUS) algorithm: methodology and applications
L Egidi, R Pappadà, N Torelli, F Pauli
Studies in Theoretical and Applied Statistics, 2018
42018
pivmet: Pivotal methods for Bayesian relabelling and k-means clustering
L Egidi, R Pappadà, F Pauli, N Torelli
arXiv preprint arXiv:2103.16948, 2021
32021
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
32018
K-means seeding via MUS algorithm
L Egidi, R Pappadà, F Pauli, N Torelli
Book of Short Papers SIS 2018, 2018
32018
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
22024
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
22023
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
22012
A Spatial AMH Copula-Based Dissimilarity Measure to Cluster Variables in Panel Data
FML Di Lascio, A Menapace, R Pappadà
12021
Assessing the number of groups in consensus clustering by pivotal methods
R Pappada, F Pauli, N Torelli
Preface XIX 1 Plenary Sessions, 132, 2021
12021
Consensus clustering via pivotal methods
L Egidi, R Pappada, F Pauli, N Torelli
COLLANA SCIENTIFICA/UNIVERSITÀ DEGLI STUDI DI CASSINO E DEL LAZIO …, 2019
12019
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