On Unified Hybrid Censored Gompertz Distribution: Application to the Wind Speed Data

A correct determination of the distribution function is very important in evaluating wind speed because it is a random phenomenon. In this paper, the Gompertz distribution is used to analyze the wind speed data under complete and unified hybrid censored samples. The maximum likelihood estimators of the unknown parameters have been evaluated via an efficient algorithm. The Bayes estimates have been developed using the Metropolis algorithm. Finally, the estimations of the parameters have been studied based on the wind speed data measured in the urban area of Iran. Different probability density functions are employed to describe this data. The quality of the data-fit is distinguished by the several tests and graphical plots. Based on these tests and plots, the Gompertz distribution seems to be the most reliable statistical distribution and thereby make better presentation of the potentials of wind energy.