One Generalized Mixture Pareto Distribution and Estimation of the Parameters by the EM Algorithm for Complete and Right-Censored Data
A new mixture generalized Pareto distribution is introduced. Then, some of its attributes are explored. The maximum likelihood method and expectation maximization (EM) algorithm have been applied to estimate the parameters for complete and right-censored data. In a simulation study, the bias, absolu...
Saved in:
| Main Author: | |
|---|---|
| Format: | article |
| Language: | EN |
| Published: |
IEEE
2021
|
| Subjects: | |
| Online Access: | https://doaj.org/article/feb870ec763b42d0a78b81cd75bb6430 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | A new mixture generalized Pareto distribution is introduced. Then, some of its attributes are explored. The maximum likelihood method and expectation maximization (EM) algorithm have been applied to estimate the parameters for complete and right-censored data. In a simulation study, the bias, absolute bias and mean squared error of the maximum likelihood estimator are compared with those related to the EM estimator. The results show that the absolute bias and mean squared error of the EM estimator are smaller than the related values for the maximum likelihood estimator. Finally, to illustrate its usefulness, the model has been applied to describe real data sets. |
|---|