Generalized modified slash Birnbaum–Saunders distribution

In this paper, a generalization of the modified slash Birnbaum-Saunders (BS) distribution is introduced. The model is defined by using the stochastic representation of the BS distribution, where the standard normal distribution is replaced by a symmetric distribution proposed by Reyes et al. It is proved that this new distribution is able to model more kurtosis than other extensions of BS previously proposed in the literature. Closed expressions are given for the pdf (probability density functio), along with their moments, skewness and kurtosis coefficients. Inference carried out is based on modified moments method and maximum likelihood (ML). To obtain ML estimates, two approaches are considered: Newton-Raphson and EM-algorithm. Applications reveal that it has potential for doing well in real problems

Small-sample corrections for score tests in Birnbaum-Saunders regressions

In this paper we deal with the issue of performing accurate small-sample inference in the Birnbaum-Saunders regression model, which can be useful for modeling lifetime or reliability data. We derive a Bartlett-type correction for the score test and numerically compare the corrected test with the usual score test, the likelihood ratio test and its Bartlett-corrected version. Our simulation results suggest that the corrected test we propose is more reliable than the other tests.Comment: To appear in the Communications in Statistics - Theory and Methods, http://www.informaworld.com/smpp/title~content=t71359723

Estimation in the Birnbaum-Saunders distribution based on scale-mixture of normals and the EM-algorithm

Scale mixtures of normal (SMN) distributions are used for modeling symmetric data. Members of this family have appealing properties such as robust estimates, easy number generation, and efficient computation of the ML estimates via the EM-algorithm. The Birnbaum-Saunders (BS) distribution is a positively skewed model that is related to the normal distribution and has received considerable attention. We introduce a type of BS distributions based on SMN models, produce a lifetime analysis, develop the EM-algorithm for ML estimation of parameters, and illustrate the obtained results with real data showing the robustness of the estimation procedure.Peer Reviewe

On an extreme value version of the Birnbaum-Saunders distribution

The Birnbaum-Saunders model is a life distribution originated from a problem of material fatigue that has been largely studied and applied in recent decades. A random variable following the Birnbaum-Saunders distribution can be stochastically represented by another random variable used as basis. Then, the Birnbaum-Saunders model can be generalized by switching the distribution of the basis variable using diverse arguments allowing to construct more general classes of models. Extreme value distributions are useful to determinate the probability of events that are more extreme than any that have already been observed. In this paper, we propose, characterize, implement and apply an extreme value version of the Birnbaum-Saunders distribution.Fundação para a Ciência e a Tecnologia (FCT) - Pluriannual Funding Program, PTDC/FEDER, PEst-OE/MAT/UI0006/2011, FCT/OE, POCI 2010FONDECYT (Fondo Nacional de Desarrollo Cient co y Tecnol ogico - Chile

Estimation in the Birnbaum-Saunders distribution based on scale-mixture of normals and the EM-algorithm

Scale mixtures of normal (SMN) distributions are used for modeling symmetric data. Members of this family have appealing properties such as robust estimates, easy number generation, and efficient computation of the ML estimates via the EM-algorithm. The Birnbaum-Saunders (BS) distribution is a positively skewed model that is related to the normal distribution and has received considerable attention. We introduce a type of BS distributions based on SMN models, produce a lifetime analysis, develop the EM-algorithm for ML estimation of parameters, and illustrate the obtained results with real data showing the robustness of the estimation procedure

Shape and change point analyses of the Birnbaum-Saunders-t hazard rate and associated estimation

The hazard rate is a statistical indicator commonly used in lifetime analysis. The Birnbaum-Saunders (BS) model is a life distribution originated from a problem pertaining to material fatigue that has been applied to diverse fields. The BS model relates the total time until failure to some type of cumulative damage that is normally distributed. The generalized BS (GBS) distribution is a class of positively skewed models with lighter and heavier tails than the BS distribution. Particular cases of GBS distributions are the BS and BS-Student-t (BS-t) models. In this paper, we discuss shape and change point analyses for the hazard rate of the BS-t distribution. In addition, we evaluate the performance of the maximum likelihood and moment estimators of this change point using Monte Carlo methods. We also present an application with a real life data set useful for survival analysis, which shows the convenience of knowing such instant of change for establishing a reduction in the dose and, as a consequence, in the cost of the treatment.FEDER Funds- Programa Operacional Factores de Competitividade - COMPETEFundação para a Ciênciae a Tecnologia (FCT) - Project Est-C/MAT/UI0013/2011FONDECYT 1120879 grant, Chil