Scaled-Invariant Extended Quasi-Lindley Model: Properties, Estimation, and Application

In many research fields, statistical probability models are often used to analyze real-world data. However, data from many fields, such as the environment, economics, and health care, do not necessarily fit traditional models. New empirical models need to be developed to improve the fit. In this study, we investigated a further extension of the quasi-Lindley model. This extension was asymmetrically distributed on the positive real number line. Maximum likelihood, least square error, Anderson-Darling, and expectation maximization algorithms were used to estimate the parameters studied. All techniques provided accurate and reliable estimates of the parameters. However, the mean square error of the expectation-maximization approach was lower. The usefulness of the proposed model was demonstrated by analyzing a reliability data set, and the analysis showed that it outperformed all other alternative models.

Automated summary

Statistical probability models are often used to analyze real-world data in many research fields. However, data from fields such as the environment, economics, and health care may not fit traditional models. This study investigates an extension of the quasi-Lindley model that is asymmetrically distributed on the positive real number line. Various algorithms are used to estimate the parameters, and the results show that all techniques provide accurate and reliable estimates. The proposed model outperforms alternative models when analyzing a reliability dataset.