| Local statistical modeling via a cluster-weighted approach with elliptical distributions S Ingrassia, SC Minotti, G Vittadini Journal of classification 29, 363-401, 2012 | 136 | 2012 |
| Constrained monotone EM algorithms for finite mixture of multivariate Gaussians S Ingrassia, R Rocci Computational Statistics & Data Analysis 51 (11), 5339-5351, 2007 | 126 | 2007 |
| Model-based clustering via linear cluster-weighted models S Ingrassia, SC Minotti, A Punzo Computational Statistics & Data Analysis 71, 159-182, 2014 | 100 | 2014 |
| A likelihood-based constrained algorithm for multivariate normal mixture models S Ingrassia Statistical Methods and Applications 13, 151-166, 2004 | 96 | 2004 |
| Neural network modeling for small datasets S Ingrassia, I Morlini Technometrics 47 (3), 297-311, 2005 | 80 | 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 | 73 | 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 | 71 | 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 | 61 | 1994 |
| Constrained monotone EM algorithms for mixtures of multivariate t distributions F Greselin, S Ingrassia Statistics and computing 20, 9-22, 2010 | 58 | 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 | 55 | 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 | 53 | 2017 |
| On parsimonious models for modeling matrix data S Sarkar, X Zhu, V Melnykov, S Ingrassia Computational Statistics & Data Analysis 142, 106822, 2020 | 52 | 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 | 46 | 2011 |
| Functional principal component analysis of financial time series S Ingrassia, GD Costanzo New Developments in Classification and Data Analysis: Proceedings of the …, 2005 | 44 | 2005 |
| Clustering bivariate mixed-type data via the cluster-weighted model A Punzo, S Ingrassia Computational Statistics 31, 989-1013, 2016 | 36 | 2016 |
| Robust estimation of mixtures of regressions with random covariates, via trimming and constraints LA García-Escudero, A Gordaliza, F Greselin, S Ingrassia, A Mayo-Íscar Statistics and Computing 27, 377-402, 2017 | 34 | 2017 |
| flexCWM: a flexible framework for cluster-weighted models A Mazza, A Punzo, S Ingrassia Journal of Statistical Software 86, 1-30, 2018 | 33 | 2018 |
| Totally coherent set-valued probability assessments A Gilio, S Ingrassia Kybernetika 34 (1), [3]-15, 1998 | 33 | 1998 |
| 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 | 31 | 2017 |
| The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers LA García-Escudero, A Gordaliza, F Greselin, S Ingrassia, A Mayo-Iscar Computational Statistics & Data Analysis 99, 131-147, 2016 | 24 | 2016 |
Antonio PunzoFull Professor of Statistics, University of CataniaVerified email at unict.it
