Classical and Bayesian Inference for the Kavya–Manoharan Generalized Exponential Distribution under Generalized Progressively Hybrid Censored Data

This manuscript focuses on the statistical inference of Kavya–Manoharan generalized exponential distribution under type-I progressive hybrid censoring sample (GTI-PHCS). Different classical approaches estimation, such as maximum likelihood, product spacing, least squares (LS), weighted LS, and percentiles GTI-PHCS, are investigated. Based squared error linear loss functions, Bayes estimates for unknown parameters utilizing separate gamma priors GTI-PHCS have been derived. Point interval developed. We carry out a simulation using Monte Carlo algorithm to show performance inferential procedures. Finally, real-world data collection is examined illustration purposes.