On a new type of Birnbaum-Saunders models and its inference and application to fatigue data

Arrué J.; Arellano-Valle R.B.; Gómez H.W.; Leiva V.

Keywords: normal distribution, Correction of bias; Monte Carlo simulation; R software; fatigue life data; maximum likelihood method; skew

Abstract

The Birnbaum-Saunders distribution is a widely studied model with diverse applications. Its origins are in the modeling of lifetimes associated with material fatigue. By using a motivating example, we show that, even when lifetime data related to fatigue are modeled, the Birnbaum-Saunders distribution can be unsuitable to fit these data in the distribution tails. Based on the nice properties of the Birnbaum-Saunders model, in this work, we use a modified skew-normal distribution to construct such a model. This allows us to obtain flexibility in skewness and kurtosis, which is controlled by a shape parameter. We provide a mathematical characterization of this new type of Birnbaum-Saunders distribution and then its statistical characterization is derived by using the maximum-likelihood method, including the associated information matrices. In order to improve the inferential performance, we correct the bias of the corresponding estimators, which is supported by a simulation study. To conclude our investigation, we retake the motivating example based on fatigue life data to show the good agreement between the new type of Birnbaum-Saunders distribution proposed in this work and the data, reporting its potential applications.

Más información

Título según SCOPUS: On a new type of Birnbaum-Saunders models and its inference and application to fatigue data
Título de la Revista: Journal of Applied Statistics
Volumen: 47
Número: 13-15
Editorial: Taylor and Francis Ltd.
Fecha de publicación: 2020
Página final: 2710
Idioma: English
DOI:

10.1080/02664763.2019.1668365

Notas: SCOPUS