Inference for Compound Exponential XLindley Model with Applications to Lifetime Data

• Published in: Symmetry (Basel) Vol. 16; no. 5; p. 625
• Main Authors: Alghamdi, Fatimah M., Meraou, Mohammed Amine, Aljohani, Hassan M., Alrumayh, Amani, Riad, Fathy H., Alsheikh, Sara Mohamed Ahmed, Alsolmi, Meshayil M.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2024
• Subjects: Bayes techniques; confidence interval; Confidence intervals; Datasets; Engineering; Entropy; Flexibility; lifetime model; Maximum likelihood estimation; Methods; Monte Carlo method; Monte Carlo simulation; Parameters; Random variables; simulation experiments; Statistical analysis; Statistics; XLindley distribution; Saudi Arabia
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym16050625

The creating of novel models essentially stems from the requirement to appropriate describe survival cases. In this study, a novel lifetime model with two parameters is proposed and studied for modeling more types of data used in different study cases, including symmetric, asymmetric, skewed, and complex datasets. The proposed model is obtained by compounding the exponential and XLindley distributions, and it is regarded as a strong competitor for the widely applied symmetrical and non-symmetrical models. Several characteristics and statistical properties are investigated. The unknown parameters of the recommended model for the complete sample are estimated using two estimation methods; notably, maximum likelihood estimation and Bayes techniques based on several loss functions as well as an approximate tool are used to construct the confidence intervals for the unknown parameters of the suggested model. The estimation procedures are compared using a Monte Carlo simulation experiment to demonstrate their effectiveness. In the end, the applicability and flexibility of the recommended model are conducted using two real lifetime datasets. In our illustration, we compare the practicality of the recommended model with several well-known competing distributions.