In order to overcome the problems due to the unboundedness of the likelihood, constrained approaches to maximum likelihood estimation in the context of finite mixtures of univariate and multivariate normals have been presented in the literature. One main drawback is that they require a knowledge of the variance and covariance structure.We propose a fully data-driven constrained method for estimation of mixtures of linear regression models. The method does not require any prior knowledge of the variance structure, it is invariant under change of scale in the data and it is easy and ready to implement in standard routines.

Finite Mixture of Linear Regression Models: An Adaptive Constrained Approach to Maximum Likelihood Estimation / Di Mari, Roberto; Rocci, Roberto; Antonio Gattone, Stefano. - (2017), pp. 181-186. (Intervento presentato al convegno SMPS 2016 tenutosi a Roma, Italia).

Finite Mixture of Linear Regression Models: An Adaptive Constrained Approach to Maximum Likelihood Estimation

Roberto Rocci;
2017

Abstract

In order to overcome the problems due to the unboundedness of the likelihood, constrained approaches to maximum likelihood estimation in the context of finite mixtures of univariate and multivariate normals have been presented in the literature. One main drawback is that they require a knowledge of the variance and covariance structure.We propose a fully data-driven constrained method for estimation of mixtures of linear regression models. The method does not require any prior knowledge of the variance structure, it is invariant under change of scale in the data and it is easy and ready to implement in standard routines.
2017
SMPS 2016
finite mixture; linear regression; maximum likelihood; constraints
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Finite Mixture of Linear Regression Models: An Adaptive Constrained Approach to Maximum Likelihood Estimation / Di Mari, Roberto; Rocci, Roberto; Antonio Gattone, Stefano. - (2017), pp. 181-186. (Intervento presentato al convegno SMPS 2016 tenutosi a Roma, Italia).
File allegati a questo prodotto
File Dimensione Formato  
DiMari_Finite-mixture_2017.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 933.46 kB
Formato Adobe PDF
933.46 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1351562
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact