EM algorithm in the slashed inverse Rayleigh regression model with an application

This paper introduced a novel distribution that generalized the inverse Rayleigh (IR) distribution. The proposed construction arose from the ratio of two independent random variables: one following the IR distribution and the other a beta distribution. Such a formulation yielded a more flexible model than the baseline IR distribution. We derived key distributional properties, proposed a regression model for the mean, and developed an expectation-maximization (EM) algorithm for maximum likelihood estimation. The finite-sample performance of the estimators was assessed via Monte Carlo simulations, and the practical utility of the model was illustrated through an application to real data.