Kayid, M.; Abouammoh, A.; Alomani, G. Scaled-Invariant Extended Quasi-Lindley Model: Properties, Estimation, and Application. Symmetry2023, 15, 1780.
Kayid, M.; Abouammoh, A.; Alomani, G. Scaled-Invariant Extended Quasi-Lindley Model: Properties, Estimation, and Application. Symmetry 2023, 15, 1780.
Kayid, M.; Abouammoh, A.; Alomani, G. Scaled-Invariant Extended Quasi-Lindley Model: Properties, Estimation, and Application. Symmetry2023, 15, 1780.
Kayid, M.; Abouammoh, A.; Alomani, G. Scaled-Invariant Extended Quasi-Lindley Model: Properties, Estimation, and Application. Symmetry 2023, 15, 1780.
Abstract
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 their fit. In this paper, we explore a further extension of the quasi-Lindley model. Maximum likelihood, least square error, Anderson-Darling algorithm, and expectation-maximization algorithm are four techniques for estimating the parameters under study. All techniques provide 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 dynamical systems data set, and the analysis shows that it outperforms the other models in all statistical models considered.
Keywords
Quasi Lindley model; maximum likelihood estimator; expectation maximization algorithm
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
