Journals Information
Mathematics and Statistics Vol. 10(2), pp. 269 - 292
DOI: 10.13189/ms.2022.100201
Reprint (PDF) (3008Kb)
Estimating Weibull Parameters Using Maximum Likelihood Estimation and Ordinary Least Squares: Simulation Study and Application on Meteorological Data
Nawal Adlina Mohd Ikbal , Syafrina Abdul Halim *, Norhaslinda Ali
Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia
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.
KEYWORDS
Maximum Likelihood Estimation, Ordinary Least Squares, Akaike Information Criterion, Bayesian Information Criterion, Anderson-Darling
Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Nawal Adlina Mohd Ikbal , Syafrina Abdul Halim , Norhaslinda Ali , "Estimating Weibull Parameters Using Maximum Likelihood Estimation and Ordinary Least Squares: Simulation Study and Application on Meteorological Data," Mathematics and Statistics, Vol. 10, No. 2, pp. 269 - 292, 2022. DOI: 10.13189/ms.2022.100201.
(b). APA Format:
Nawal Adlina Mohd Ikbal , Syafrina Abdul Halim , Norhaslinda Ali (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. DOI: 10.13189/ms.2022.100201.