Salvatore Ingrassia
Salvatore Ingrassia
Full Professor of Statistics, UniversitÓ di Catania (Italy)
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Local statistical modeling via a cluster-weighted approach with elliptical distributions
S Ingrassia, SC Minotti, G Vittadini
Journal of classification 29, 363-401, 2012
Constrained monotone EM algorithms for finite mixture of multivariate Gaussians
S Ingrassia, R Rocci
Computational Statistics & Data Analysis 51 (11), 5339-5351, 2007
Model-based clustering via linear cluster-weighted models
S Ingrassia, SC Minotti, A Punzo
Computational Statistics & Data Analysis 71, 159-182, 2014
A likelihood-based constrained algorithm for multivariate normal mixture models
S Ingrassia
Statistical Methods and Applications 13, 151-166, 2004
Neural network modeling for small datasets
S Ingrassia, I Morlini
Technometrics 47 (3), 297-311, 2005
Erratum to: The generalized linear mixed cluster-weighted model
S Ingrassia, A Punzo, G Vittadini, SC Minotti
Journal of Classification 32, 327-355, 2015
Clustering and classification via cluster-weighted factor analyzers
S Subedi, A Punzo, S Ingrassia, PD McNicholas
Advances in Data Analysis and Classification 7 (1), 5-40, 2013
On the rate of convergence of the Metropolis algorithm and Gibbs sampler by geometric bounds
S Ingrassia
The Annals of Applied Probability, 347-389, 1994
Constrained monotone EM algorithms for mixtures of multivariate t distributions
F Greselin, S Ingrassia
Statistics and computing 20, 9-22, 2010
Cluster-weighted -factor analyzers for robust model-based clustering and dimension reduction
S Subedi, A Punzo, S Ingrassia, PD McNicholas
Statistical Methods & Applications 24 (4), 623-649, 2015
Multivariate response and parsimony for Gaussian cluster-weighted models
UJ Dang, A Punzo, PD McNicholas, S Ingrassia, RP Browne
Journal of Classification 34, 4-34, 2017
On parsimonious models for modeling matrix data
S Sarkar, X Zhu, V Melnykov, S Ingrassia
Computational Statistics & Data Analysis 142, 106822, 2020
Degeneracy of the EM algorithm for the MLE of multivariate Gaussian mixtures and dynamic constraints
S Ingrassia, R Rocci
Computational statistics & data analysis 55 (4), 1715-1725, 2011
Functional principal component analysis of financial time series
S Ingrassia, GD Costanzo
New Developments in Classification and Data Analysis: Proceedings of theá…, 2005
Clustering bivariate mixed-type data via the cluster-weighted model
A Punzo, S Ingrassia
Computational Statistics 31, 989-1013, 2016
Robust estimation of mixtures of regressions with random covariates, via trimming and constraints
LA GarcÝa-Escudero, A Gordaliza, F Greselin, S Ingrassia, A Mayo-═scar
Statistics and Computing 27, 377-402, 2017
flexCWM: a flexible framework for cluster-weighted models
A Mazza, A Punzo, S Ingrassia
Journal of Statistical Software 86, 1-30, 2018
The effect of ISM absorption on stellar activity measurements and its relevance for exoplanet studies
L Fossati, SE Marcelja, D Staab, PE Cubillos, K France, CA Haswell, ...
Astronomy & Astrophysics 601, A104, 2017
Totally coherent set-valued probability assessments
A Gilio, S Ingrassia
Kybernetika 34 (1), [3]-15, 1998
A comparison between the simulated annealing and the EM algorithms in normal mixture decompositions
S Ingrassia
Statistics and Computing 2, 203-211, 1992
Il sistema al momento non pu˛ eseguire l'operazione. Riprova pi¨ tardi.
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