Estimation of the Parameters by Using non-Bayesian in Modified Weibull Extension Distribution

Shaima'a Yass; Iden Al Kanani

Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9 September 2021, Sakarya, Turkey

Research Article

Estimation of the Parameters by Using non-Bayesian in Modified Weibull Extension Distribution

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Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/eai.7-9-2021.2314887,
    author={Shaima'a B. Yass and Iden H. Al Kanani},
    title={Estimation of the Parameters by Using non-Bayesian in Modified Weibull Extension Distribution},
    proceedings={Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9 September 2021, Sakarya, Turkey},
    publisher={EAI},
    proceedings_a={IMDC-IST},
    year={2022},
    month={1},
    keywords={modified weibull extension classical distribution methods newton raphson method},
    doi={10.4108/eai.7-9-2021.2314887}
}

Shaima'a B. Yass
Iden H. Al Kanani
Year: 2022
Estimation of the Parameters by Using non-Bayesian in Modified Weibull Extension Distribution
IMDC-IST
EAI
DOI: 10.4108/eai.7-9-2021.2314887

Shaima'a B. Yass¹^,*, Iden H. Al Kanani²

1: Al-Iraqi University, College of Engineering, Networks Engineering Department
2: The University of Baghdad, College of Science for Women, Department of Mathematics

*Contact email: shai.bader@yahoo.com

Abstract

In this paper, studying the modified Weibull extension model, and derive the estimators of three parameters by using the maximum likelihood method, and ordinary least squares estimator method. Then by utilizing the Monte Carlo method in the simulation procedure to generate many various samples sizes with many various of replicate the sample size. Finally, finding that the maximum likelihood method is the best value from the ordinary least squares’ method.

Keywords: modified weibull extension classical distribution methods newton raphson method

Published: 2022-01-26
Publisher: EAI

: http://dx.doi.org/10.4108/eai.7-9-2021.2314887