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

  • Roberto Di MariEmail author
  • Roberto Rocci
  • Stefano Antonio Gattone
Conference paper
  • 1.1k Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 456)

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.

Keywords

Likelihood Function Linear Regression Model Finite Mixture Scale Balance Likelihood Principle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Roberto Di Mari
    • 1
    Email author
  • Roberto Rocci
    • 1
  • Stefano Antonio Gattone
    • 2
  1. 1.DEF, University of Tor VergataRomeItaly
  2. 2.DiSFPEQ, University G. d’AnnunzioChieti-PescaraItaly

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