Volume 12, Issue 2, July 2013, Pages 152 - 172
Kernel Inference on the Generalized Gamma Distribution Based on Generalized Order Statistics
M. Ahsanullah, M. Maswadah, Ali M. Seham
Received 6 January 2013, Accepted 16 March 2013, Available Online 15 July 2013.
- https://doi.org/10.2991/jsta.2013.12.2.3How to use a DOI?
- Generalized gamma distribution; Generalized order statistics; Maximum likelihood estimation; Kernel density estimation; Asymptotic maximum likelihood estimations.
- The kernel approach has been applied using the adaptive kernel density estimation, to inference on the generalized gamma distribution parameters, based on the generalized order statistics (GOS). For measuring the performance of this approach comparing to the Asymptotic Maximum likelihood estimation, the confidence intervals of the unknown parameters have been studied, via Monte Carlo simulations, based on their covering rates, standard errors and the average lengths. The simulation results indicated that the confidence intervals based on the kernel approach compete and outperform the classical ones. Finally, a numerical example is given to illustrate the proposed approaches developed in this paper.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - M. Ahsanullah AU - M. Maswadah AU - Ali M. Seham PY - 2013 DA - 2013/07 TI - Kernel Inference on the Generalized Gamma Distribution Based on Generalized Order Statistics JO - Journal of Statistical Theory and Applications SP - 152 EP - 172 VL - 12 IS - 2 SN - 2214-1766 UR - https://doi.org/10.2991/jsta.2013.12.2.3 DO - https://doi.org/10.2991/jsta.2013.12.2.3 ID - Ahsanullah2013 ER -