Estimation of the Generalized Logarithmic Transformation Exponential Distribution under Progressively Type-II Censored Data with Application to the COVID-19 Mortality Rates

In this paper, classical and Bayesian estimation for the parameters reliability function generalized logarithmic transformation exponential (GLTE) distribution has been proposed when life-times are progressively censored. The maximum likelihood estimator of unknown their corresponding obtained under setup. Bayes estimators symmetric (squared error) asymmetric (LINEX general entropy) loss functions. This was achieved by considering discrete prior scale parameter conditional gamma shape parameter. Interval schemes is also considered. performances various derived recorded using simulation study different sample sizes progressive censoring schemes. Finally, COVID-19 mortality data sets provided to illustrate computation estimators.