‎In this paper‎, ‎we introduce a new integer-valued autoregressive model of first order based on the negative binomial thinning operator‎, ‎where the noises are serially dependent‎. ‎Some statistical properties of the model are discussed‎. ‎The model parameters are estimated by maximum likelihood and Yule-Walker methods‎. ‎By a simulation study‎, ‎the performances of the two estimation methods are studied‎. ‎This survey was carried out to study the efficiency of the new model by applying it on real data‎.

Wind speed probabilistic distributions are one of the main wind characteristics for the evaluation of wind energy potential in a specific region.  In this paper, 3-parameter Log-Logistic distribution is introduced and it compared with six used statistical models for the modeling the actual wind speed data reported of Tabriz and Orumiyeh stations in Iran. The maximum likelihood estimators method via Nelder–Mead algorithm is utilized for estimating the model parameters. The flexibility of proposed distributions is measured according to the coefficient of determination, Chi-square test, Kolmogorov-Smirnov test, and root mean square error criterion. Results of the analysis show that 3-parameter Log-Logistic distribution provides the best fit to model the annual and seasonal wind speed data in Orumiyeh station and except summer season for Tabriz station. Also, wind power density error is estimated for the proposed different distributions.