Reliability Estimation Techniques for the Generalized Inverted Exponential Distribution Under Improved Adaptive Type‐II Progressive Censoring

ABSTRACT This paper examines the estimation of parameters, reliability, and hazard function of the generalized inverted exponential distribution within an improved adaptive Type‐II progressive censoring framework. Point estimates are derived through frequentist and Bayesian methodologies employing the Expectation‐Maximization (EM) algorithm, Markov chain Monte Carlo (MCMC), and TK approximation, while interval estimates are established utilizing the asymptotic normality property of maximum likelihood estimates (MLE) and MCMC samples. A generalized entropy loss function is considered, and extensive simulations are conducted to compare the effectiveness of the proposed methodologies. Additionally, two real datasets are analyzed to illustrate the practical applicability of our study.