International Journal of Applied Mathematics, Computational Science and Systems Engineering
E-ISSN: 2766-9823
Volume 8, 2026
Kernel Inference on the Entropy and Parameters of the Inverse Weibull Distribution Using Dual Generalized Progressive Hybrid Censored Data
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Abstract: In statistical inference, entropy quantifies the uncertainty associated with a random variable. This study introduces a novel numerical iteration technique specifically, a kernel estimation method for estimating both the entropy and the parameters of the inverse Weibull distribution. The proposed kernel method is implemented and its performance is compared with that of the Bayesian approach via Monte Carlo simulations. The simulation results indicate that the kernel method yields more accurate estimates and outperforms the Bayesian approach under the dual generalized progressive hybrid censored data. Finally, the practical applicability of the proposed method is illustrated and validated through the analysis of two real datasets.
Pages: 101-115
DOI: 10.37394/232026.2026.8.10