%0 Journal Article %1 Qin2012Two %A Qin, Xu %A Zhang, Jiang-she %A Yan, Xiao-dong %B Journal of Applied Meteorology and Climatology %D 2012 %I American Meteorological Society %J J. Appl. Meteor. Climatol. %K wind statistics %N 7 %P 1321--1332 %R 10.1175/jamc-d-11-0231.1 %T Two Improved Mixture Weibull Models for the Analysis of Wind Speed Data %U http://dx.doi.org/10.1175/jamc-d-11-0231.1 %V 51 %X Abstract In this paper, the authors propose two improved mixture Weibull distribution models by adding one or two location parameters to the existing two-component mixture two-parameter Weibull distribution MWbl(2, 2) model. One improved model is the mixture two-parameter Weibull and three-parameter Weibull distribution MWbl(2, 3) model. The other improved model is the two-component mixture three-parameter Weibull distribution MWbl(3, 3) model. In contrast to existing literature, which has focused on the MWbl(2, 2) and the typical Weibull distribution models, the authors apply the MWbl(2, 3) model and MWbl(3, 3) model to fit the distribution of wind speed data with nearly zero percentages of null wind speed. The parameters of the two improved models are estimated by the maximum likelihood method in which the maximization problem is regarded as a nonlinear programming problem with only inequality constraints and is solved numerically by the interior-point method. The experimental results show that the mixture Weibull models proposed in this paper are more flexible than the existing models for the analysis of wind speed data in practice. Abstract In this paper, the authors propose two improved mixture Weibull distribution models by adding one or two location parameters to the existing two-component mixture two-parameter Weibull distribution MWbl(2, 2) model. One improved model is the mixture two-parameter Weibull and three-parameter Weibull distribution MWbl(2, 3) model. The other improved model is the two-component mixture three-parameter Weibull distribution MWbl(3, 3) model. In contrast to existing literature, which has focused on the MWbl(2, 2) and the typical Weibull distribution models, the authors apply the MWbl(2, 3) model and MWbl(3, 3) model to fit the distribution of wind speed data with nearly zero percentages of null wind speed. The parameters of the two improved models are estimated by the maximum likelihood method in which the maximization problem is regarded as a nonlinear programming problem with only inequality constraints and is solved numerically by the interior-point method. The experimental results show that the mixture Weibull models proposed in this paper are more flexible than the existing models for the analysis of wind speed data in practice.

@article{Qin2012Two, abstract = {Abstract In this paper, the authors propose two improved mixture Weibull distribution models by adding one or two location parameters to the existing two-component mixture two-parameter Weibull distribution [MWbl(2, 2)] model. One improved model is the mixture two-parameter Weibull and three-parameter Weibull distribution [MWbl(2, 3)] model. The other improved model is the two-component mixture three-parameter Weibull distribution [MWbl(3, 3)] model. In contrast to existing literature, which has focused on the MWbl(2, 2) and the typical Weibull distribution models, the authors apply the MWbl(2, 3) model and MWbl(3, 3) model to fit the distribution of wind speed data with nearly zero percentages of null wind speed. The parameters of the two improved models are estimated by the maximum likelihood method in which the maximization problem is regarded as a nonlinear programming problem with only inequality constraints and is solved numerically by the interior-point method. The experimental results show that the mixture Weibull models proposed in this paper are more flexible than the existing models for the analysis of wind speed data in practice. Abstract In this paper, the authors propose two improved mixture Weibull distribution models by adding one or two location parameters to the existing two-component mixture two-parameter Weibull distribution [MWbl(2, 2)] model. One improved model is the mixture two-parameter Weibull and three-parameter Weibull distribution [MWbl(2, 3)] model. The other improved model is the two-component mixture three-parameter Weibull distribution [MWbl(3, 3)] model. In contrast to existing literature, which has focused on the MWbl(2, 2) and the typical Weibull distribution models, the authors apply the MWbl(2, 3) model and MWbl(3, 3) model to fit the distribution of wind speed data with nearly zero percentages of null wind speed. The parameters of the two improved models are estimated by the maximum likelihood method in which the maximization problem is regarded as a nonlinear programming problem with only inequality constraints and is solved numerically by the interior-point method. The experimental results show that the mixture Weibull models proposed in this paper are more flexible than the existing models for the analysis of wind speed data in practice.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Qin, Xu and Zhang, Jiang-she and Yan, Xiao-dong}, biburl = {https://www.bibsonomy.org/bibtex/2967a2d6a311333a02ed2891d3e0441c9/pbett}, booktitle = {Journal of Applied Meteorology and Climatology}, citeulike-article-id = {11021744}, citeulike-linkout-0 = {http://journals.ametsoc.org/doi/abs/10.1175/JAMC-D-11-0231.1}, citeulike-linkout-1 = {http://dx.doi.org/10.1175/jamc-d-11-0231.1}, day = 27, doi = {10.1175/jamc-d-11-0231.1}, interhash = {c37a2011eb45c222f49ffb14fe979321}, intrahash = {967a2d6a311333a02ed2891d3e0441c9}, journal = {J. Appl. Meteor. Climatol.}, keywords = {wind statistics}, month = apr, number = 7, pages = {1321--1332}, posted-at = {2012-08-06 12:18:42}, priority = {2}, publisher = {American Meteorological Society}, timestamp = {2018-06-22T18:32:31.000+0200}, title = {Two Improved Mixture Weibull Models for the Analysis of Wind Speed Data}, url = {http://dx.doi.org/10.1175/jamc-d-11-0231.1}, volume = 51, year = 2012 }