| Autor: |
Naderi, Mehrdad, Mozafari, Mahdieh, Okhli, Kheirolah |
| Zdroj: |
Journal of Statistical Theory and Applications; March 2020, Vol. 19 Issue: 1 p49-58, 10p |
| Abstrakt: |
Finite mixture model is a widely acknowledged model-based clustering method for analyzing data. In this paper, a new finite mixture model via an extension of Birnbaum-Saunders distribution is introduced. The new mixture model provide a useful generalization of the heavy-tailed lifetime model since the mixing components cover both skewness and kurtosis. Some properties and characteristics of the model are derived and an expectation and maximization (EM)-type algorithm is developed to compute maximum likelihood estimates. The asymptotic standard errors of the parameter estimates are obtained via offering an information-based approach. Finally, the performance of the methodology is illustrated by considering both simulated and real datasets. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
|
Full text from SpringerLink