Abstract
The type II half logistic length-biased exponential distribution, which extends the length-biased exponential model, is introduced and examined. This model's statistical features, such as moments, conditional moments, mean order statistics, and entropy, are derived. To estimate distribution parameters, statistical inference employs three estimation methods: maximum likelihood, Cramer-von Mises, and Anderson-Darling. To evaluate the performance of the aforementioned estimators, we run simulation experiments based on the graphical results. The TIIHLLBE model's adaptability has been demonstrated through applications to real-world datasets.