Giuliano Galimberti

According to our database, Giuliano Galimberti authored at least 12 papers between 2005 and 2023.

Collaborative distances:

• Dijkstra number of six.
• Erdős number of six.

2023

Semiparametric finite mixture of regression models with Bayesian P-splines.

Marco Berrettini, Giuliano Galimberti, Saverio Ranciati

Adv. Data Anal. Classif., September, 2023

2022

Multivariate cluster-weighted models based on seemingly unrelated linear regression.

Cecilia Diani, Giuliano Galimberti, Gabriele Soffritti

Comput. Stat. Data Anal., 2022

2020

Seemingly unrelated clusterwise linear regression.

Giuliano Galimberti, Gabriele Soffritti

Adv. Data Anal. Classif., 2020

2018

Modelling the role of variables in model-based cluster analysis.

Giuliano Galimberti, Annamaria Manisi, Gabriele Soffritti

Stat. Comput., 2018

2016

Using mixtures in seemingly unrelated linear regression models with non-normal errors.

Giuliano Galimberti, Elena Scardovi, Gabriele Soffritti

Stat. Comput., 2016

2014

A multivariate linear regression analysis using finite mixtures of t distributions.

Giuliano Galimberti, Gabriele Soffritti

Comput. Stat. Data Anal., 2014

2013

Discussion of "Model-based clustering and classification with non-normal mixture distributions" by S.X. Lee and G.J. McLachlan.

Giuliano Galimberti, Angela Montanari

Stat. Methods Appl., 2013

Using conditional independence for parsimonious model-based Gaussian clustering.

Giuliano Galimberti, Gabriele Soffritti

Stat. Comput., 2013

2011

Multivariate linear regression with non-normal errors: a solution based on mixture models.

Gabriele Soffritti, Giuliano Galimberti

Stat. Comput., 2011

2009

Penalized factor mixture analysis for variable selection in clustered data.

Giuliano Galimberti, Angela Montanari, Cinzia Viroli

Comput. Stat. Data Anal., 2009

2007

Model-based methods to identify multiple cluster structures in a data set.

Giuliano Galimberti, Gabriele Soffritti

Comput. Stat. Data Anal., 2007

2005

Identifying Multiple Cluster Structures Through Latent Class Models.

Giuliano Galimberti, Gabriele Soffritti

Proceedings of the From Data and Information Analysis to Knowledge Engineering, 2005