377 research outputs found
Flexible Birnbaum-Saunders distribution
In this paper, we propose a bimodal extension of the Birnbaum–Saunders model by including an extra parameter. This new model is termed flexible Birnbaum–Saunders (FBS) and includes the ordinary Birnbaum–Saunders (BS) and the skew Birnbaum–Saunders (SBS) model as special cases. Its properties are studied. Parameter estimation is considered via an iterative maximum likelihood approach. Two real applications, of interest in environmental sciences, are included, which reveal that our proposal can perform better than other competing models.Ministerio de Economía y Competitividad (MINECO). Españ
Testing hypotheses in the Birnbaum-Saunders distribution under type-II censored samples
The two-parameter Birnbaum-Saunders distribution has been used succesfully to
model fatigue failure times. Although censoring is typical in reliability and
survival studies, little work has been published on the analysis of censored
data for this distribution. In this paper, we address the issue of performing
testing inference on the two parameters of the Birnbaum-Saunders distribution
under type-II right censored samples. The likelihood ratio statistic and a
recently proposed statistic, the gradient statistic, provide a convenient
framework for statistical inference in such a case, since they do not require
to obtain, estimate or invert an information matrix, which is an advantage in
problems involving censored data. An extensive Monte Carlo simulation study is
carried out in order to investigate and compare the finite sample performance
of the likelihood ratio and the gradient tests. Our numerical results show
evidence that the gradient test should be preferred. Three empirical
applications are presented.Comment: Submitted for publicatio
Birnbaum-Saunders spatial modelling and diagnostics applied to agricultural engineering data
Applications of statistical models to describe spatial dependence in geo-referenced data are widespread across many disciplines including the environmental sciences. Most of these application assume that the data follow a Gaussian distributions. However, in many of them the normality assumption, and even a more general assumption of symmetry, are not appropriate. In non-spatial applications, where the data are uni-modal and positively skewed, the Birnbaum-Saunders distribution has excelled. This paper proposes a spatial log-linear model based in the Birnbaum-Saunders distribution. Model parameters are estimated using the maximum likelihood method. Local influence diagnostics are derived to assess the sensitivity of the estimators to perturbations in the response variable. As illustration, the proposed model and its diagnostics are used to analyse a real-world agricultural data-set, where the spatial variability of phosphorus concentration in the soil is considered- which is extremely important for agricultural management
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