A log Birnbaum-Saunders regression model based on the skew-normal distribution under the centred parameterization

摘要

In this paper we introduce a new regression model for positive and skewed data, a log Birnbaum-Saunders model based on the centred skew-normal distribution, also presenting several inference tools for this model. Initially, we developed a new version of the skew-sinh-normal distribution, describing some of its properties. For the proposed regression model, we carry out, through the expectation conditional maximization (ECM) algorithm, parameter estimation, model fit assessment, model comparison and residual analysis. Finally, our model accommodates more suitably the asymmetry of the data, compared with the usual log Birnbaum-Saunders model, which is illustrated through a real data analysis.