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Scale-constrained approaches for maximum likelihood estimation and model selection of clusterwise linear regression models

  • Roberto Di MariEmail author
  • Roberto Rocci
  • Stefano Antonio Gattone
Original Paper
  • 26 Downloads

Abstract

We consider an equivariant approach imposing data-driven bounds for the variances to avoid singular and spurious solutions in maximum likelihood estimation of clusterwise linear regression models. We investigate its use in the choice of the number of components and we propose a computational shortcut, which significantly reduces the computational time needed to tune the bounds on the data. In the simulation study and the two real-data applications, we show that the proposed methods guarantee a reliable assessment of the number of components compared to standard unconstrained methods, together with accurate model parameters estimation and cluster recovery.

Keywords

Clusterwise linear regression Mixtures of linear regression models Data-driven constraints Equivariant estimators Computationally efficient approach Model selection 

Notes

References

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Economics and BusinessUniversity of CataniaCataniaItaly
  2. 2.Department of Economics and FinanceUniversity of Rome Tor VergataRomeItaly
  3. 3.Department of Philosophical and Social Sciences, Economics and Quantitative MethodsUniversity G. d’AnnunzioChieti-PescaraItaly

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