Search

This paper proposed a novel distribution parameterized by four parameters. This is achieved by compounding the potentials properties of the Type I half logistic topp-leone generalized distribution family with the properties of the inverse lomax distribution to form the novel Type I half logistic topp-leone inverse lomax distribution. The novel distribution is potentially capable of extending classical inverse lomax distribution. The potentiality of the shape of the probability density function of the novel distribution is worth recognizing since it produces right skewed, approximately normal, left skewed and a reverted J-shaped. Decreasing life failure shape is also observed. Distinctive features of the novel distribution such as moments, entropy, moment generating function, reliability and hazard function were derived. The estimation method explored in this study is maximum likelihood estimation. It is adopted to estimate the novel distribution unknown parameters. Real life data set was adopted to investigate the potentiality and applicability of the novel model. The type I half logistic topp-leone inverse lomax distribution outperform the recent models. [ABSTRACT FROM AUTHOR]

This paper aims to propose six methods of parameter estimation in order to examine the behavior of the new Type I Half Logistic Topp-leone Exponential distribution. The methods taking into consideration are Maximum Likelihood, Anderson_Darling, Least Squares, Cramer_von_Mises, Maximum Product of Spacing, and Weighted Least Squares Methods. The results show that all the methods are consistent, since the estimates approach the true value of the parameters for all the methods. The bias, mean square error and mean relative estimates decay as the sample size is raised. The estimates of the six methods obtained for the model, indicated that MPS estimates is the closest to the true value of the parameters across the low, moderate and high sample sizes, invariable, the MPS produces the least biasness. Buttress more, the MPS produces the least MSE all through and remain the best estimator for low, moderate and high sample size of the model. Conclusively, MPS is the most consistent among the estimators for the model. [ABSTRACT FROM AUTHOR]