Abstract
A new generalized family of distributions referred to as Exponentiated-Gompertz-Marshall-Olkin-G (EGom- MO-G) distribution is introduced. The distribution can be expressed as an infinite linear combination of the exponentiated-G family of distributions. Some mathematical properties are derived and studied. Several estimation techniques including maximum likelihood estimation, Cramér-von Mises, least squares estimation, weighted least squares, Anderson-Darling and right-tail Anderson-Darling methods are compared. A special case of the new family of distributions is adopted for application to two real data sets and compared to some existing models. Results revealed that the new family of distributions is superior than compared models.
| Original language | English |
|---|---|
| Pages (from-to) | 1752-1788 |
| Number of pages | 37 |
| Journal | Statistics, Optimization and Information Computing |
| Volume | 13 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2025 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Statistics and Probability
- Information Systems
- Computer Vision and Pattern Recognition
- Statistics, Probability and Uncertainty
- Control and Optimization
- Artificial Intelligence