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pro vyhledávání: '"Rocci, Roberto"'
The standard mixture modelling framework has been widely used to study heterogeneous populations, by modelling them as being composed of a finite number of homogeneous sub-populations. However, the standard mixture model assumes that each data point
Externí odkaz:
http://arxiv.org/abs/2409.01874
Autor:
Carleo, Alessandra, Rocci, Roberto
Publikováno v:
In Socio-Economic Planning Sciences October 2024 95
Autor:
Rocci, Roberto1 (AUTHOR), Gattone, Stefano A.2 (AUTHOR) gattone@unich.it
Publikováno v:
Journal of Computational & Graphical Statistics. Nov2024, p1-22. 22p. 10 Illustrations.
Akademický článek
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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 method for maximum likelihood estimation of clusterwise linear regression i
Externí odkaz:
http://arxiv.org/abs/1611.03309
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 likelihood, together with the presence of spurious maximizers. Existing met
Externí odkaz:
http://arxiv.org/abs/1609.03317
Autor:
Ranalli, Monia, Rocci, Roberto
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 difficult by the presence of noise variables/dimensions that do not cont
Externí odkaz:
http://arxiv.org/abs/1504.02913
Autor:
Ranalli, Monia, Rocci, Roberto
Publikováno v:
Advances in Data Analysis & Classification; Jun2024, Vol. 18 Issue 2, p381-407, 27p
Publikováno v:
In International Journal of Approximate Reasoning December 2017 91:160-178
Autor:
Ranalli, Monia, Rocci, Roberto
Publikováno v:
In Computational Statistics and Data Analysis June 2017 110:87-102