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