Inferences for generalized Topp-Leone distribution under dual generalized order statistics with applications to Engineering and COVID-19 data
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Date
2021
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Abstract
This article accentuates the estimation of a two-parameter generalized Topp-Leone distribution using dual generalized
order statistics (dgos). In the part of estimation, we obtain maximum likelihood (ML) estimates and approximate confidence
intervals of the model parameters using dgos, in particular, based on order statistics and lower record values. The Bayes estimate
is derived with respect to a squared error loss function using gamma priors. The highest posterior density credible interval is
computed based on the MH algorithm. Furthermore, the explicit expressions for single and product moments of dgos from this
distribution are also derived. Based on order statistics and lower records, a simulation study is carried out to check the efficiency
of these estimators. Two real life data sets, one is for order statistics and another is for lower record values have been analyzed to
demonstrate how the proposed methods may work in practice.