Inferences for generalized Topp-Leone distribution under dual generalized order statistics with applications to Engineering and COVID-19 data

This article accentuates the estimation of a two-parameter generalized Topp-Leone distribution using dual order statistics (dgos). In part estimation, we obtain maximum likelihood (ML) estimates and approximate confidence intervals model parameters dgos, in particular, based on lower record values. The Bayes estimate is derived with respect to squared error loss function gamma priors. highest posterior density credible interval computed MH algorithm. Furthermore, explicit expressions for single product moments dgos from this are also derived. Based records, simulation study carried out check efficiency these estimators. Two real life data sets, one another values have been analyzed demonstrate how proposed methods may work practice.