TY - JOUR
T1 - Marshall-Olkin Extended Inverse Weibull Distribution and Its Application
AU - Pakungwati, R. M.
AU - Widyaningsih, Y.
AU - Lestari, D.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - Weibull distribution is the most popular distribution in wind speed energy literature. However, in real life, the wind speed data may not always modelled by Weibull distribution. An alternative in modeling wind speed data is the inverse Weibull distribution. Inverse Weibull distribution is modifications from the Weibull distribution with the transformation variables. Marshall and Olkin (1997) introduced an interesting method of adding a parameter to a well established distribution so we extend the Invers Weibull distribution by the Marshall-Olkin method (IWMO). The probability density function (pdf), cumulative distribution function (cdf), hazard rate, survival function, moment and quantiles of IWMO are derived. We also discuss the estimation of the model parameters by maximum likelihood. The IWMO distribution was applied to wind speed data. The results were given which illustrate the IWMO distribution and were compared to Weibull distribution and Inverse Weibull distribution. Model comparison using the log likelihood, AIC, and BIC showed that IWMO fit the data better than the other distributions.
AB - Weibull distribution is the most popular distribution in wind speed energy literature. However, in real life, the wind speed data may not always modelled by Weibull distribution. An alternative in modeling wind speed data is the inverse Weibull distribution. Inverse Weibull distribution is modifications from the Weibull distribution with the transformation variables. Marshall and Olkin (1997) introduced an interesting method of adding a parameter to a well established distribution so we extend the Invers Weibull distribution by the Marshall-Olkin method (IWMO). The probability density function (pdf), cumulative distribution function (cdf), hazard rate, survival function, moment and quantiles of IWMO are derived. We also discuss the estimation of the model parameters by maximum likelihood. The IWMO distribution was applied to wind speed data. The results were given which illustrate the IWMO distribution and were compared to Weibull distribution and Inverse Weibull distribution. Model comparison using the log likelihood, AIC, and BIC showed that IWMO fit the data better than the other distributions.
UR - http://www.scopus.com/inward/record.url?scp=85058329360&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1108/1/012114
DO - 10.1088/1742-6596/1108/1/012114
M3 - Conference article
AN - SCOPUS:85058329360
SN - 1742-6588
VL - 1108
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012114
T2 - 2nd Mathematics, Informatics, Science and Education International Conference, MISEIC 2018
Y2 - 21 July 2018
ER -