Abstract
In this research, the application of Exponentiated Burr-V (EBV) distribution is presented and compared with other competing distributions which include Exponentiated Pareto distribution, Exponentiated Lomax distribution, Exponentiated Gumbel distribution, and Exponentiated Generalized Inverse Exponential distribution. The maximum likelihood estimation method was used in estimating the EBV and the four competing distributions. The loglikelihood (LL) and Akaike Information Criteria (AIC) was applied for determining the best fitted distribution and the distribution with the largest LL and smallest AIC is considered the best fitted distribution. The data used is on bladder cancer which is a widely used data from Lee and Wang (2003), Lemonte and Cordeiro (2011), Luz, (2012), and Kazeem et al., (2014). The results obtained showed that the EBV distribution has the largest LL value of 2950.726 and the smallest AIC value of –5895.452. The LL and AIC values imply that the EBV distribution is a very competitive distribution in fitting the bladder cancer data and it is an appropriate distribution for fitting asymmetric or negatively skewed and high kurtosis datasets.