WEIGHTED CROSS VALIDATION IN THE SELECTION OF ROBUST REGRESSION MODEL WITH CHANGE-POINT FOR TELEVISION RATING FORECAST

Carlos Alberto Huaira Contreras, Carlos Cristiano Hasenclever Borges, Camila Borelli Zeller, Amanda Romanelli

Resumo


The paper proposes a weighted cross-validation (WCV) algorithm  to select a linear regression model with change-point under a scale mixtures of normal (SMN) distribution that yields the best prediction results. SMN distributions are used to construct robust regression models to the influence of outliers on the parameter estimation process. Thus, we relaxed the usual assumption of normality of the regression models and considered that the random errors follow a SMN distribution, specifically the Student-t distribution. In addition, we consider the fact that the parameters of the regression model can change from a specific and unknown point, called change-point. In this context, the estimations of the model parameters, which include the change-point, are obtained via the EM-type algorithm (Expectation-Maximization). The WCV method is used in the selection of the model that presents greater robustness and that offers a smaller prediction error, considering that the weighting values come from step E of the EM-type algorithm. Finally, numerical examples considering simulated and real data (data from television audiences) are presented to illustrate the proposed methodology.

Palavras-chave


Cross-validation; Regression model; EM-type algorithm; Student-t distribution; Change-point

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DOI: http://dx.doi.org/10.21575/25254782rmetg2020vol5n21179

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Direitos autorais 2020 Carlos Alberto Huaira Contreras, Carlos Cristiano Hasenclever Borges, Camila Borelli Zeller, Amanda Romanelli

Revista Mundi Engenharia, Tecnologia e Gestão ISSN 2525-4782

Qualis: B4 - Interdisciplinar, B5 - Geografia, B5 - Administração Pública e de Empresas, Ciências Contábeis e Turismo, B5 - Comunicação e Informação, B5 - Engenharias III