Kernel Inference on the Generalized Gamma Distribution Based on Generalized Order Statistics

DOI

10.2991/jsta.2013.12.2.3How to use a DOI?

Keywords

Generalized gamma distribution; Generalized order statistics; Maximum likelihood estimation; Kernel density estimation; Asymptotic maximum likelihood estimations.

Abstract

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - M. Ahsanullah
AU  - M. Maswadah
AU  - Ali M. Seham
PY  - 2013
DA  - 2013/07/15
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  - 10.2991/jsta.2013.12.2.3
ID  - Ahsanullah2013
ER  -