Information and Uncertainty Analysis of the OBXII-IRD Using Seven Measures of Entropy with Application to Carbon Fibers Data
Alaa Abdulrahman Khalaf, Mundher A. Khaleel, Ahmed M. Salih, Ali T. Mohammed, Moudher Kh. Abdal-Hammed, Qasim N. Husain
Source abstract
Introduction: This research studies the Odd Burr Inverse Rayleigh (OBXII-IRD) distribution, concentrating on parameter estimation methods and entropy measures. Methodology: Three estimation methods: Maximum likelihood estimation, maximum product of spacings, and Kolmogorov were used and their outcomes were investigated using Monte Carlo simulation. Results: The results demonstrated their ability to estimate parameters reasonably well. Seven entropy measures including Renyi, Shannon, Tsallis, Arimoto, Havrda-Charvat, Sharma-Mittal, and Sant’ Anna-Taneja were subject to distribution’s uncertainty. To show its practical perfomance, the OBXII-IRD was utilized to real-world data representing carbon fibers. Conclusion: The results indicate that the OBXII-IRD is superior and fits the data better than other distributions. The statistical software R was used for all programming and computational tasks in this study.
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