Simulation Assessment of Expectation-maximization Algorithm in Pseudo-convex Mixtures Generated by the Exponential Distribution

The use of pseudo-convex mixtures generated from stable distributions for extremes offers a valuable approach for handling reliability-related data challenges. This framework encompasses pseudo-convex mixtures stemming from exponential distribution. However, precise parameter estimation, particularly in cases where the weight parameter ω is negative, remains a challenge. This work assesses the performance of the Expectation- Maximization algorithm in estimating parameters for pseudo-convex mixtures generated by the exponential distribution through simulation.