Comparison of four methods for estimating the Weibull distribution parameters

Pobocikova, Ivana; Michalková, Mária

doi:10.12988/ams.2014.45389

Cited by 31 publications

(31 citation statements)

References 9 publications

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“…It follows that the average WPD associated with the PDF of the wind speed random variable is given as Observe also that the average WPD given by (40) is obtained by computing the third moment about zero of the wind speed random variable . If follows the Weibull distribution, one readily obtain the WPD using (24) as The cube of the mean wind speed for a wind speed random variable following the Weibull distribution is given by It follows that the EPF for a wind speed random variable following the Weibull is given by Equating the population moments which make up (43) to their corresponding sample moments we obtain…”

Section: Energy Pattern Factor Methods (Epfm)mentioning

confidence: 99%

An appraisal on some methods for estimating the 2-parameter weibull distribution with application to wind speeds sample

Osatohanmwen

Oyegue

Osemwenkhae

et al. 2017

Sri Lankan J App Stats

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Six methods for estimating the Weibull shape and scale parameters are considered and compared in this paper. These methods are: the least squares method, weighted least squares method, method of moments, energy pattern factor method, method of L-moments and the maximum likelihood method. A simulation study as well as application to a real data set (wind speeds sample) is used to test the performance of the different methods using the smallest mean square error criterion. Results from the simulation study indicate that the maximum likelihood method is the most efficient method when dealing with large sample sizes, while the weighted least squares method, method of moments and the method of L-moments are quite efficient for small and moderate sample sizes. The maximum likelihood method produced the best method when all six methods were applied to a wind speeds sample by possessing the smallest mean square error. A very useful result obtained from the study is that the weighted least squares method, performed considerably well in estimating the Weibull parameters. This is a rare incidence in many studies.

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Section: Energy Pattern Factor Methods (Epfm)mentioning

confidence: 99%

An appraisal on some methods for estimating the 2-parameter weibull distribution with application to wind speeds sample

Osatohanmwen

Oyegue

Osemwenkhae

et al. 2017

Sri Lankan J App Stats

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“…Pobočíková & Sedliačková [12] have compared four methods for parameters estimation of Weibull distribution namely least squares, weighted least squares, maximum likelihood and method of moments. The numerical results indicated that the method of moments and maximum likelihood provide equivalent results but they recommended maximum likelihood because of its optimal properties.…”

Section: Maximum Likelihood Estimation Is Considered As Most Importanmentioning

confidence: 99%

Efficient estimation of Pareto model using modified maximum likelihood estimators

et al. 2018

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“…is the Euler's Gamma function. To estimate the parameters , k λ , exists various methods like the mean squared method or the moment method [16].…”

Section: Marginals Under Studymentioning

confidence: 99%

Modeling Wind Energy Using Copula

Bahraoui¹,

Bahraoui²,

Bahraoui³

2018

OALib

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In most studies related to wind energy, the quantity of the air density is considered constant, but actually, we know that it is variable and depending on others natural factors. We present a new procedure to estimate the wind density power energy by simulating the components of the air density. The procedure uses the copula theory and demonstrates that the estimated power energy is higher if the air density is not constant.

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Comparison of four methods for estimating the Weibull distribution parameters

Cited by 31 publications

References 9 publications

An appraisal on some methods for estimating the 2-parameter weibull distribution with application to wind speeds sample

An appraisal on some methods for estimating the 2-parameter weibull distribution with application to wind speeds sample

Efficient estimation of Pareto model using modified maximum likelihood estimators

Modeling Wind Energy Using Copula

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