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Estimating weibull parameters using maximum likelihood estimation and ordinary least squares: simulation study and application on meteorological data


Citation

Mohd Ikbal, Nawal Adlina and Abdul Halim, Syafrina and Ali, Norhaslinda (2022) Estimating weibull parameters using maximum likelihood estimation and ordinary least squares: simulation study and application on meteorological data. Mathematics and Statistics, 10 (2). 269 - 292. ISSN 2332-2071; ESSN: 2332-2144

Abstract

Inefficient estimation of distribution parameters for current climate will lead to misleading results in future climate. Maximum likelihood estimation (MLE) is widely used to estimate the parameters. However, MLE is not well performed for the small size. Hence, the objective of this study is to compare the efficiency of MLE with ordinary least squares (OLS) through the simulation study and real data application on wind speed data based on model selection criteria, Akaike information criterion (AIC) and Bayesian information criterion (BIC) values. The Anderson-Darling (AD) test is also performed to validate the proposed distribution. In summary, OLS is better than MLE when dealing with small sample sizes of data and estimating the shape parameter, while MLE is capable of estimating the value of scale parameter. However, both methods are well performed at a large sample size.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Science
DOI Number: https://doi.org/10.13189/ms.2022.100201
Publisher: Horizon Research Publishing Corporation
Keywords: Maximum likelihood estimation; Ordinary least squares; Akaike information criterion; Bayesian information criterion; Anderson-Darling
Depositing User: Ms. Che Wa Zakaria
Date Deposited: 22 Sep 2023 23:29
Last Modified: 22 Sep 2023 23:29
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.13189/ms.2022.100201
URI: http://psasir.upm.edu.my/id/eprint/101304
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