A new OLS-based procedure for clusterwise linear regression

Gratis

digital

A new OLS-based procedure for clusterwise linear regression

Articolo

Rivista

STATISTICA & APPLICAZIONI

Sezione

Open Access

Titolo

A new OLS-based procedure for clusterwise linear regression

Autori

Salvatore Bologna, Antonella Plaia

Editore

Vita e Pensiero

Formato

Articolo |

Pdf

Online da

01-2009

Issn

1824-6672 (stampa) | 2283-6659 (digitale)

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Descrizione Commenti dei lettori

Data heterogeneity, within a (linear) regression framework, often suggest the use of a Clusterwise Linear Regression (CLR) procedure, which implies, among other things, the estimate of the appropriate number of clusters as well as the cluster membership of each unit. The approaches to the estimation of a CLR model are essentially based on the Ordinary Least Square (OLS) criterion or the likelihood criterion. In this paper, in a context of OLS approach, we propose an estimation of the model making use of an algorithm based on a threshold criterion for the determination coefficient of each cluster, to identify the appropriate number of clusters, and of a modified Spath’s algorithm, to estimate the cluster membership of each sample unit. A simulation design and an application to a real data-set show that the procedure outperforms other algorithms commonly used in literature.

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