ENHANCING LINDLEY DISTRIBUTION PARAMETER ESTIMATION WITH HYBRID BAYESIAN AVERAGE MODEL FOR FUZZY DATA

With the ultimate goal of increasing parameter estimate accuracy, this study will examine and assess a number of estimating techniques used with the Lindley distribution in the context of fuzzy data. Gibbs sampling, Bootstrapping Sampling, MCMC, MH, and a unique hybrid methodology that combines these approaches via Bayesian model averaging were also studied. The research looks at several sample sizes ranging from 15 to 100 and repeats the estimate method 10,000 times for each size. Fuzzy data are created using established fuzzy systems, and the performance of each approach is measured using average values (AV), mean squared errors (MSE), coverage probabilities, and confidence interval lengths. The findings show that the hybrid technique consistently produces estimates closer to the genuine parameter value of one across all sample sizes, with smaller mean squared errors than individual methods. Furthermore, the hybrid method’s confidence intervals preserve coverage probabilities that are consistent with the targeted confidence level, demonstrating the method’s trustworthiness in statistical inference. Overall, the results show that the hybrid technique improves estimate accuracy and reliability, providing a strong foundation for parameter estimation in the Lindley distribution framework using fuzzy data.