Influence diagnostics in generalized log-gamma regression models
Computational Statistics & Data Analysis
Improved statistical inference for the two-parameter Birnbaum-Saunders distribution
Computational Statistics & Data Analysis
Influence diagnostics in log-Birnbaum-Saunders regression models with censored data
Computational Statistics & Data Analysis
Mixture cure models for multivariate survival data
Computational Statistics & Data Analysis
On the hazard function of Birnbaum-Saunders distribution and associated inference
Computational Statistics & Data Analysis
Log-Burr XII regression models with censored data
Computational Statistics & Data Analysis
Log-modified Weibull regression models with censored data: Sensitivity and residual analysis
Computational Statistics & Data Analysis
Birnbaum-Saunders nonlinear regression models
Computational Statistics & Data Analysis
A log-extended Weibull regression model
Computational Statistics & Data Analysis
The log-exponentiated Weibull regression model for interval-censored data
Computational Statistics & Data Analysis
A hands-on approach for fitting long-term survival models under the GAMLSS framework
Computer Methods and Programs in Biomedicine
Improved likelihood inference in Birnbaum-Saunders regressions
Computational Statistics & Data Analysis
Size and power properties of some tests in the Birnbaum-Saunders regression model
Computational Statistics & Data Analysis
The β-Birnbaum-Saunders distribution: An improved distribution for fatigue life modeling
Computational Statistics & Data Analysis
Hi-index | 0.03 |
The @b-Birnbaum-Saunders (Cordeiro and Lemonte, 2011) and Birnbaum-Saunders (Birnbaum and Saunders, 1969a) distributions have been used quite effectively to model failure times for materials subject to fatigue and lifetime data. We define the log-@b-Birnbaum-Saunders distribution by the logarithm of the @b-Birnbaum-Saunders distribution. Explicit expressions for its generating function and moments are derived. We propose a new log-@b-Birnbaum-Saunders regression model that can be applied to censored data and be used more effectively in survival analysis. We obtain the maximum likelihood estimates of the model parameters for censored data and investigate influence diagnostics. The new location-scale regression model is modified for the possibility that long-term survivors may be presented in the data. Its usefulness is illustrated by means of two real data sets.