The use of multi-parameter distribution functions that incorporate empirically derived parameters to more accurately capture the nature of data being studied is investigated. Improving the accuracy of these models is especially important for predicting the extreme values of the non-linear random variables. This study was motivated by problems commonly encountered in the design of offshore syste...

A genetic algorithm (GA) is used to solve the redundancy allocation problem when the objective is to maximize a lower percentile of the system time-to-failure distribution and the available components have random Weibull scale parameters. The GA searches the prospective solution space using an adaptive penalty to consider both feasible and infeasible solutions until converging to a feasible rec...

This paper presents algorithms for generating random variables for exponential/Rayleigh/ Weibull, Nakagami-m and Rician copulas with any desired copula parameter(s), using the direct conditional cumulative distribution function method and the complex Gaussian distribution method. Moreover, a novel method for optimal copula selection is also proposed, based on the criterion that for a given seri...

This article deals with the Bayesian inference of the unknown parameters of the Weibull distribution based on the left truncated and right censored data. It is assumed that the scale parameter of the Weibull distribution has a gamma prior. The shape parameter may be known or unknown. If the shape parameter is unknown, it is assumed that it has a very general log-concave prior distribution. When...

We exhibit a new stability property of Weibull tail-distributions. It is shown that the power of a random variable with a Weibull tail-distribution still has a Weibull tail-distribution and the associated Weibull tail-coefficient is exhibited. Weibull tail-distributions are defined through their distribution function by: