Estimating weibull parameters using maximum likelihood estimation and ordinary least squares: simulation study and application on meteorological data
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 comp...
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| Format: | Article |
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Horizon Research Publishing Corporation
2022
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| author | Mohd Ikbal, Nawal Adlina Abdul Halim, Syafrina Ali, Norhaslinda |
| author_facet | Mohd Ikbal, Nawal Adlina Abdul Halim, Syafrina Ali, Norhaslinda |
| author_sort | Mohd Ikbal, Nawal Adlina |
| collection | UPM |
| description | 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. |
| first_indexed | 2024-03-06T11:15:02Z |
| format | Article |
| id | upm.eprints-101304 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T11:15:02Z |
| publishDate | 2022 |
| publisher | Horizon Research Publishing Corporation |
| record_format | dspace |
| spelling | upm.eprints-1013042023-09-22T23:29:27Z http://psasir.upm.edu.my/id/eprint/101304/ Estimating weibull parameters using maximum likelihood estimation and ordinary least squares: simulation study and application on meteorological data Mohd Ikbal, Nawal Adlina Abdul Halim, Syafrina Ali, Norhaslinda 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. Horizon Research Publishing Corporation 2022 Article PeerReviewed 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 https://www.hrpub.org/journals/article_info.php?aid=11835 10.13189/ms.2022.100201 |
| spellingShingle | Mohd Ikbal, Nawal Adlina Abdul Halim, Syafrina Ali, Norhaslinda Estimating weibull parameters using maximum likelihood estimation and ordinary least squares: simulation study and application on meteorological data |
| title | Estimating weibull parameters using maximum likelihood estimation and ordinary least squares: simulation study and application on meteorological data |
| title_full | Estimating weibull parameters using maximum likelihood estimation and ordinary least squares: simulation study and application on meteorological data |
| title_fullStr | Estimating weibull parameters using maximum likelihood estimation and ordinary least squares: simulation study and application on meteorological data |
| title_full_unstemmed | Estimating weibull parameters using maximum likelihood estimation and ordinary least squares: simulation study and application on meteorological data |
| title_short | Estimating weibull parameters using maximum likelihood estimation and ordinary least squares: simulation study and application on meteorological data |
| title_sort | estimating weibull parameters using maximum likelihood estimation and ordinary least squares simulation study and application on meteorological data |
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