Influence diagnostics in log-Birnbaum-Saunders regression models with censored data
Computational Statistics & Data Analysis
Lifetime analysis based on the generalized Birnbaum-Saunders distribution
Computational Statistics & Data Analysis
On the hazard function of Birnbaum-Saunders distribution and associated inference
Computational Statistics & Data Analysis
On Birnbaum-Saunders inference
Computational Statistics & Data Analysis
An R implementation for generalized Birnbaum-Saunders distributions
Computational Statistics & Data Analysis
The Birnbaum-Saunders autoregressive conditional duration model
Mathematics and Computers in Simulation
The β-Birnbaum-Saunders distribution: An improved distribution for fatigue life modeling
Computational Statistics & Data Analysis
Estimation of extreme percentiles in Birnbaum-Saunders distributions
Computational Statistics & Data Analysis
Diagnostic procedures in Birnbaum-Saunders nonlinear regression models
Computational Statistics & Data Analysis
Robust statistical modeling using the Birnbaum-Saunders-t distribution applied to insurance
Applied Stochastic Models in Business and Industry
Generalized Birnbaum-Saunders kernel density estimators and an analysis of financial data
Computational Statistics & Data Analysis
Generalized multivariate Birnbaum-Saunders distributions and related inferential issues
Journal of Multivariate Analysis
A robust extension of the bivariate Birnbaum-Saunders distribution and associated inference
Journal of Multivariate Analysis
Lower confidence limit for reliability based on grouped data using a quantile-filling algorithm
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
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.