Influence diagnostics in log-Birnbaum–Saunders regression models with censored data

In this paper we discuss log-Birnbaum–Saunders regression models with censored observations. This kind of model has been largely applied to study material lifetime subject to failure or stress. The score functions and observed Fisher information matrix are given as well as the process for estimating the regression coefficients and shape parameter is discussed. The normal curvatures of local influence are derived under various perturbation schemes and two deviance-type residuals are proposed to assess departures from the log-Birnbaum–Saunders error assumption as well as to detect outlying observations. Finally, a data set from the medical area is analyzed under log-Birnbaum–Saunders regression models. A diagnostic analysis is performed in order to select an appropriate model.