Bayesian inference for the parameters of the log-extended exponential-geometric distribution via ranked set sampling

This paper discusses the parameter estimation of the log-extended exponential-geometric distribution using ranked set sampling and maximum ranked set sampling with unequal samples. The maximum likelihood and Bayesian methods are used to estimate the distribution parameters. Markov Chain Monte Carlo techniques are used to obtain the Bayes estimators and the corresponding credible intervals under a squared error loss function. Moreover, the estimators are compared with the Monte Carlo simulation study. At last, a real data set is analyzed for demonstration purposes.