Bayesian estimation of a geometric distribution using informative priors based on a Type-I censoring scheme

In this paper, the geometric distribution parameter is estimated under a type-I censoring scheme by means of Bayesian estimation approach. The Beta and Kumaraswamy informative priors, as well five loss functions are used for purpose. Expressions Bayes estimators risks derived Squared Error Loss Function (SELF), Quadratic (QLF), Precautionary (PLF), Simple Asymmetric (SAPLF), DeGroot (DLF) using two aforementioned priors. prior densities obtained through predictive distributions. Simulation studies carried out to make comparisons risks. Finally, real-life data example verify model’s efficiency.