International Journal of Computational and Applied Mathematics & Computer Science
E-ISSN: 2769-2477
Volume 6, 2026
Conditional Inference on the Weibull Distribution Parameters Using Generalized Progressive Hybrid Censored Data
Author:
Search Articles
Abstract: It is well known that conditional confidence intervals for unknown distribution parameters are often
as efficient as Bayesian confidence intervals based on non-informative priors. Accordingly, the main objective
of this work is to derive conditional point estimates for the unknown parameters of the Weibull distribution using
pivotal functions, and to compare them with Bayesian point estimates through Monte Carlo simulations. The
simulation results indicate that, under the generalized progressive hybrid-censoring scheme, the conditional point
estimates are highly efficient and outperform their Bayesian counterparts. Finally, the proposed methods are
applied to real data to illustrate their practical effectiveness.
Pages: 21-32
DOI: 10.37394/232028.2026.6.3