Journal of Statistical Theory and Applications

Volume 16, Issue 1, March 2017, Pages 81 - 95

A study of methods for estimating in the exponentiated Gumbel distribution

Authors
K. Fathi, S.F. Bagheri, M. Alizadeh, M. Alizadeh
Corresponding Author
K. Fathi
Received 18 August 2015, Accepted 24 October 2016, Available Online 1 March 2017.
DOI
10.2991/jsta.2017.16.1.7How to use a DOI?
Keywords
Uniform minimum variance unbiased estimator; Maximum likelihood estimator; Least squares estimator;Weight least squares estimator; Percentile estimator; Model selection criteria; Exponentiated Gumbel distribution.
Abstract

The exponentiated Gumbel model has been shown to be useful in climate modeling including global warming problem, flood frequency analysis, offshore modeling, rainfall modeling and wind speed modeling. Here, we consider estimation of the PDF and the CDF of the exponentiated Gumbel distribution. The following esti- mators are considered: uniformly minimum variance unbiased (UMVU) estimator, maximum likelihood (ML) estimator, percentile (PC) estimator, least squares (LS) estimator and weighted least squares (WLS) estimator. Analytical expressions are derived for the bias and the mean squared error. Simulation studies and real data applications show that the ML estimator performs better than others.

Copyright
© 2017, 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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Journal
Journal of Statistical Theory and Applications
Volume-Issue
16 - 1
Pages
81 - 95
Publication Date
2017/03/01
ISSN (Online)
2214-1766
ISSN (Print)
1538-7887
DOI
10.2991/jsta.2017.16.1.7How to use a DOI?
Copyright
© 2017, 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/).

Cite this article

TY  - JOUR
AU  - K. Fathi
AU  - S.F. Bagheri
AU  - M. Alizadeh
AU  - M. Alizadeh
PY  - 2017
DA  - 2017/03/01
TI  - A study of methods for estimating in the exponentiated Gumbel distribution
JO  - Journal of Statistical Theory and Applications
SP  - 81
EP  - 95
VL  - 16
IS  - 1
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2017.16.1.7
DO  - 10.2991/jsta.2017.16.1.7
ID  - Fathi2017
ER  -