Estimation Methods: Inference Classical and Bayesian of Extended Inverse Exponential Distribution
DOI:
https://doi.org/10.62933/g25zz869Keywords:
Inverse Exponential, , MCMC, Bayesian estimator, general entropyAbstract
The parameters of the Burr XII-Inverse Exponential (BXII-IE) distribution were estimated in this work using a variety of Bayesian and non-Bayesian (Inference Classical) approaches. Methods like Maximum likelihood, least squares, weighted least squares, Maximum product space estimators, and Anderson Darling were developed for non-Bayesian estimators. Due to the lack of closed-form solutions for Bayesian estimates for certain loss functions squared error, general entropy, and linear-exponential and prior distributions for the parameters, Bayesian estimators had to be implemented. Bayesian estimation utilizing the Markov Chain Monte Carlo (MCMC) approach and seven non-Bayesian estimate techniques were tested for performance.
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