S. Ingrassia

发表

ON THE RATE OF CONVERGENCE OF THE METROPOLIS ALGORITHM AND GIBBS SAMPLER BY GEOMETRIC BOUNDS

S. Ingrassia, 1994 .

A comparison between the simulated annealing and the EM algorithms in normal mixture decompositions

S. Ingrassia, 1992 .

A likelihood-based constrained algorithm for multivariate normal mixture models

S. Ingrassia, 2004 .

ON THE DEGREES OF FREEDOM IN RICHLY PARAMETERISED MODELS

S. Ingrassia, I. Morlini, 2004 .

A More Informative Approach to Compare Scedasticity under the Assumption of Multivariate Normality

S. Ingrassia, A. Punzo, F. Greselin, 2009 .

Monotone Constrained EM Algorithms for Multinormal Mixture Models

R. Rocci, S. Ingrassia, 2006 .

Advances in Data Analysis and Classification

G. McLachlan, G. Govaert, S. Ingrassia, 2015 .

Geometrical Aspects of Discrimination by Multilayer Perceptrons

Salvatore Ingrassia, S. Ingrassia, 1999 .

SOME REMARKS ON NONLINEAR RELATIONSHIPS IN PLS PATH MODELING

S. Ingrassia, L. Trinchera, 2010 .

The 2nd Special issue on Mixture Models

S. Ingrassia, Tsung-I Lin, 2022, Econometrics and Statistics.

Modeling Return to Education in Heterogeneous Populations: An Application to Italy

Salvatore Ingrassia, Antonio Punzo, Angelo Mazza, 2017, Statistical Learning of Complex Data.

A mixture model approach to spectral clustering and application to textual data

S. Ingrassia, Cinzia Di Nuzzo, 2022, Statistical Methods & Applications.

Editorial for the 4th Special Issue on advances in mixture models

John Hinde, Paul D. McNicholas, Tsung-I Lin, 2019, Comput. Stat. Data Anal..

Special issue on “Model-based clustering and classification”

Hans-Hermann Bock, Salvatore Ingrassia, Jeroen K. Vermunt, 2013, Adv. Data Anal. Classif..

Multivariate Response and Parsimony for Gaussian Cluster-Weighted Models

Ryan P. Browne, P. McNicholas, S. Ingrassia, 2017, Journal of Classification.

The Generalized Linear Mixed Cluster-Weighted Model

S. Ingrassia, A. Punzo, G. Vittadini, 2015, Journal of Classification.

Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions

S. Ingrassia, G. Vittadini, S. Minotti, 2012, Journal of Classification.

Cluster Validation for Mixtures of Regressions via the Total Sum of Squares Decomposition

S. Ingrassia, A. Punzo, 2019, Journal of Classification.

Clustering bivariate mixed-type data via the cluster-weighted model

S. Ingrassia, A. Punzo, 2015, Computational Statistics.

Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions

S. Ingrassia, A. Maruotti, A. Punzo, 2019, Statistical Papers.

Clustering and classification via cluster-weighted factor analyzers

P. McNicholas, S. Ingrassia, A. Punzo, 2013, Advances in Data Analysis and Classification.

Robust estimation of mixtures of regressions with random covariates, via trimming and constraints

S. Ingrassia, A. Gordaliza, L. García-Escudero, 2016, Statistics and Computing.

On the Use of the Sub-Gaussian $$\alpha $$α-Stable Distribution in the Cluster-Weighted Model

S. Ingrassia, A. Punzo, A. Mohammadpour, 2019 .

Multivariate Response and Parsimony for Gaussian Cluster-Weighted Models

Ryan P. Browne, Paul D. McNicholas, Utkarsh J. Dang, 2014, Journal of Classification.

Modelling students’ career indicators via mixtures of parsimonious matrix‐normal distributions

S. Ingrassia, Volodymyr Melnykov, S. Tomarchio, 2022, Australian & New Zealand Journal of Statistics.

Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions

Giorgio Vittadini, Salvatore Ingrassia, Simona C. Minotti, 2012, J. Classif..

Functional Principal Component Analysis of Financial Time Series

Salvatore Ingrassia, G. Damiana Costanzo, S. Ingrassia, 2005 .

Assessing the pattern of covariance matrices via an augmentation multiple testing procedure

Salvatore Ingrassia, Antonio Punzo, Francesca Greselin, 2011, Stat. Methods Appl..

Erratum to: The Generalized Linear Mixed Cluster-Weighted Model

Giorgio Vittadini, Salvatore Ingrassia, Antonio Punzo, 2015, Journal of Classification.

Neural Network Modeling for Small Datasets

Salvatore Ingrassia, Isabella Morlini, S. Ingrassia, 2005, Technometrics.

Cluster Validation for Mixtures of Regressions via the Total Sum of Squares Decomposition

Salvatore Ingrassia, Antonio Punzo, S. Ingrassia, 2020, J. Classif..

flexCWM: A Flexible Framework for Cluster-Weighted Models

Salvatore Ingrassia, Antonio Punzo, Angelo Mazza, 2018 .

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population

Salvatore Ingrassia, Antonello Maruotti, Antonio Punzo, 2018, Statistics in medicine.

Multilevel cluster-weighted models for the evaluation of hospitals

Giorgio Vittadini, Salvatore Ingrassia, Antonio Punzo, 2016 .

Clustering bivariate mixed-type data via the cluster-weighted model

Salvatore Ingrassia, Antonio Punzo, S. Ingrassia, 2016, Comput. Stat..

Decision boundaries for mixtures of regressions

Salvatore Ingrassia, Antonio Punzo, S. Ingrassia, 2016 .

The Generalized Linear Mixed Cluster-Weighted Model

Giorgio Vittadini, Salvatore Ingrassia, Antonio Punzo, 2015, Journal of Classification.

Cluster-weighted $$t$$t-factor analyzers for robust model-based clustering and dimension reduction

Paul D. McNicholas, Salvatore Ingrassia, Antonio Punzo, 2015, Stat. Methods Appl..

Parsimonious Generalized Linear Gaussian Cluster-Weighted Models

Salvatore Ingrassia, Antonio Punzo, S. Ingrassia, 2015 .

Model-based clustering via linear cluster-weighted models

Salvatore Ingrassia, Antonio Punzo, Simona C. Minotti, 2012, Comput. Stat. Data Anal..

Clustering and classification via cluster-weighted factor analyzers

Paul D. McNicholas, Salvatore Ingrassia, Antonio Punzo, 2012, Advances in Data Analysis and Classification.

Constrained monotone EM algorithms for finite mixture of multivariate Gaussians

Salvatore Ingrassia, Roberto Rocci, R. Rocci, 2007, Comput. Stat. Data Anal..

Eigenvalues and constraints in mixture modeling: geometric and computational issues

Luis Angel García-Escudero, Salvatore Ingrassia, Agustín Mayo-Íscar, 2017, Advances in Data Analysis and Classification.

Eigenvalues and constraints in mixture modeling: geometric and computational issues

S. Ingrassia, A. Gordaliza, L. García-Escudero, 2017, Advances in Data Analysis and Classification.

The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers

Luis Angel García-Escudero, Salvatore Ingrassia, Agustín Mayo-Íscar, 2016, Comput. Stat. Data Anal..

Maximum likelihood estimation in constrained parameter spaces for mixtures of factor analyzers

Salvatore Ingrassia, Francesca Greselin, S. Ingrassia, 2013, Stat. Comput..

Degeneracy of the EM algorithm for the MLE of multivariate Gaussian mixtures and dynamic constraints

Salvatore Ingrassia, Roberto Rocci, R. Rocci, 2011, Comput. Stat. Data Anal..

Constrained monotone EM algorithms for mixtures of multivariate t distributions

Salvatore Ingrassia, Francesca Greselin, S. Ingrassia, 2010, Stat. Comput..

On parsimonious models for modeling matrix data

Salvatore Ingrassia, Volodymyr Melnykov, Xuwen Zhu, 2020, Comput. Stat. Data Anal..

Geometric Approaches to the Estimation of the Spectral Gap of Reversible Markov Chains

Salvatore Ingrassia, S. Ingrassia, 1993, Combinatorics, Probability and Computing.

Local and Overall Deviance R-Squared Measures for Mixtures of Generalized Linear Models

S. Ingrassia, A. Punzo, Roberto Di Mari, 2023, Journal of Classification.

Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions

Salvatore Ingrassia, Antonello Maruotti, Antonio Punzo, 2019 .

Robust estimation for mixtures of Gaussian factor analyzers, based on trimming and constraints

A. Gordaliza, A. Mayo-Iscar, L. A. Garc'ia-Escudero, 2015, 1503.06302.

Generalized Linear Gaussian Cluster-Weighted Modeling

Giorgio Vittadini, Salvatore Ingrassia, Antonio Punzo, 2012, 1211.1171.

Constrained EM Algorithms for Gaussian Mixtures of Factor Analyzers

Salvatore Ingrassia, Francesca Greselin, S. Ingrassia, 2012 .

Maximum Likelihood Estimation of Gaussian Cluster Weighted Models and Relationships with Mixtures of Regression

S. Ingrassia, S. Minotti, 2012, 1207.3106.

Local statistical modeling by cluster-weighted

S. Ingrassia, G. Vittadini, S. Minotti, 2009, 0911.2634.

Weakly Homoscedastic Constraints for Mixtures of t-Distributions

Salvatore Ingrassia, Francesca Greselin, S. Ingrassia, 2008, GfKl.

COMPUTATIONAL STUDIES WITH EQUIVALENT DEGREES OF FREEDOMS IN NEURAL NETWORKS

Salvatore Ingrassia, Isabella Morlini, Corso Italia, 2009 .

Maximum likelihood estimation in constrained parameter spaces for mixtures of factor analyzers

S. Ingrassia, F. Greselin, 2013, Statistics and Computing.

Multilevel cluster-weighted models for the evaluation of hospitals

S. Ingrassia, A. Punzo, G. Vittadini, 2016, METRON.

Advances in Data Analysis and Classification

G. McLachlan, G. Govaert, S. Ingrassia, 2015 .

Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions

S. Ingrassia, G. Vittadini, S. Minotti, 2012, Journal of Classification.