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Influence analysis in skew-Birnbaum–Saunders regression models and applications

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In this paper, we propose a method to assess influence in skew-Birnbaum–Saunders regression models, which are an extension based on the skew-normal distribution of the usual Birnbaum–Saunders (BS) regression model. An interesting characteristic that the new regression model has is the capacity of predicting extreme percentiles, which is not possible with the BS model. In addition, since the observed likelihood function associated with the new regression model is more complex than that from the usual model, we facilitate the parameter estimation using a type-EM algorithm. Moreover, we employ influence diagnostic tools that considers this algorithm. Finally, a numerical illustration includes a brief simulation study and an analysis of real data in order to show the proposed methodology.
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Keywords: EM algorithm; extreme percentiles; local influence; sinh-normal distribution; skew-normal distribution

Document Type: Research Article

Affiliations: 1: Departamento de Estatística,Universidade Estadual de Campinas, Casilla 5030São Paulo, Brazil 2: Departamento de Estadística,CIMFAV, Universidad de Valparaíso, Casilla 5030Valparaíso, Chile

Publication date: August 1, 2011

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