Nonlinear regression models based on the normal mean-variance mixture of Birnbaum-Saunders distribution

This paper presents a new extension of nonlinear regression models constructed by assuming the normal mean-variance mixture of Birnbaum-Saunders distribution for the unobserved error terms. A computationally analytical EM-type algorithm is developed for computing maximum likelihood estimates. The observed information matrix is derived for obtaining the asymptotic standard errors of parameter estimates. The practical utility of the methodology is illustrated through both simulated and real data sets.