Parameter Estimation of the Burr Type XII Distribution with a Progressively Interval-Censored Scheme Using Genetic Algorithm

Burr type XII distribution (BXIID) has been widely used to model lifetime data sets. The flexibility of the BXIID is established due to its two shape parameters. To save test time and cost, the BXIID parameters can be inferred by using the maximum likelihood estimation method based on a date set with incomplete lifetime information. But the maximum likelihood estimates (MLEs) of BXIID parameters could have a big bias and mean squared error (MSE) if the sample size is small or the MLEs are evaluated with improper initial parameters. In this study, a progressively interval-censored (PIC) scheme is used to implement the life test, and the Genetic Algorithm (GA) is applied to reduce the bias and MSEs of the MLEs of the BXIID parameters. An extensive Monte Carlo simulation was conducted to evaluate the estimation performance of the typical maximum likelihood estimation method (TMLEM) and GA. Simulation results show that the GA is competitive with the TMLEM in terms of resulting in a smaller bias and MSE in parameter estimation.