1 citations · 3 across the 4 of their papers we have counts for
4 papers
Constrained maximum likelihood estimation of clusterwise linear regression models with unknown number of components
R. Di Mari, R. Rocci, S. A. Gattone
We consider an equivariant approach imposing data-driven bounds for the variances to avoid singular and spurious solutions in maximum likelihood (ML) estimation of clusterwise line…
Estimation of clusterwise linear regression models with a shrinkage-like approach
Roberto Di Mari, Roberto Rocci, Stefano Antonio Gattone
Constrained approaches to maximum likelihood estimation in the context of finite mixtures of normals have been presented in the literature. A fully data-dependent constrained metho…
A data driven equivariant approach to constrained Gaussian mixture modeling
Roberto Rocci, Stefano Antonio Gattone, Roberto Di Mari
Maximum likelihood estimation of Gaussian mixture models with different class-specific covariance matrices is known to be problematic. This is due to the unboundedness of the likel…
A pairwise likelihood approach to simultaneous clustering and dimensional reduction of ordinal data
Monia Ranalli, Roberto Rocci
The literature on clustering for continuous data is rich and wide; differently, that one developed for categorical data is still limited. In some cases, the problem is made more di…