Confidence intervals for the difference between the coefficients of variation of Weibull distributions for analyzing wind speed dispersion.

Manussaya La-Ongkaew, Sa-Aat Niwitpong, Suparat Niwitpong

Abstract read

In one paragraph

Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed

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3 citing papers in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Manussaya La-OngkaewDepartment of Applied Statistics, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.

Sa-Aat NiwitpongDepartment of Applied Statistics, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.

Suparat NiwitpongDepartment of Applied Statistics, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wind energy is an important renewable energy source for generating electricity that has the potential to replace fossil fuels. Herein, we propose confidence intervals for the difference between the coefficients of variation of Weibull distributions constructed using the concepts of the generalized confidence interval (GCI), Bayesian methods, the method of variance estimates recovery (MOVER) based on Hendricks and Robey's confidence interval, a percentile bootstrap method, and a bootstrap method with standard errors. To analyze their performances, their coverage probabilities and expected lengths were evaluated via Monte Carlo simulation. The simulation results indicate that the coverage probabilities of GCI were greater than or sometimes close to the nominal confidence level. However, when the Weibull shape parameter was small, the Bayesian- highest posterior density interval was preferable. All of the proposed confidence intervals were applied to wind speed data measured at 90-meter wind energy potential stations at various regions in Thailand.

Indexed as

Bayesian methodsCoefficients of variationDispersionGeneralized confidence intervalGibbs algorithmMarkov chain Monte CarloRandom walk MetropolisThe method of variance estimates recoveryWind speed data

Identifiers

PMID34249509

DOI10.7717/peerj.11676

PMCPMC8256813

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