E-Bayesian Estimation of Exponential-Lomax Distribution Under Asymmetric Loss Functions

Exponential and Lomax has wide applications in statistical practices because of their wide mathematical tracability. They are used in modelling the business failure time data, survival analysis as well as modelling the income data. Exponential distribution exhibits a constant failure rate and also has the fundamental property of being memoryless. The Lomax distribution is the special case of Pareto distribution for which the support begins at zero and also known as Pareto-II distribution. In this paper, we have estimated the unknown scale parameter of Exponential-Lomax Distribution using Bayesian and E-Bayesian estimation under Degroot, Al-Bayyati and Minimum-Expected loss functions by considering gamma as a conjugate prior distribution. A Monte Carlo simulation technique has been used to obtain the numerical results of Bayesian and E-Bayesian estimates along with their respective MSEs that has been obtained by using MatLab.