Two-Parameter Stochastic Weibull Diffusion Model: Statistical Inference and Application to Real Modeling Example
Ahmed Nafidi,
Meriem Bahij,
Ramón Gutiérrez-Sánchez,
Boujemâa Achchab
Affiliations
Ahmed Nafidi
Department of mathematics and informatics, LAMSAD, National School of Applied Sciences of Berrechid, University of Hassan 1, Avenue de l’université, BP 280, Berrechid 26100, Morocco
Meriem Bahij
Department of mathematics and informatics, LAMSAD, National School of Applied Sciences of Berrechid, University of Hassan 1, Avenue de l’université, BP 280, Berrechid 26100, Morocco
Ramón Gutiérrez-Sánchez
Department of Statistics and Operational Research, University of Granada, Facultad de Ciencias, Campus de Fuentenueva, 18071 Granada, Spain
Boujemâa Achchab
Department of mathematics and informatics, LAMSAD, National School of Applied Sciences of Berrechid, University of Hassan 1, Avenue de l’université, BP 280, Berrechid 26100, Morocco
This paper describes the use of the non-homogeneous stochastic Weibull diffusion process, based on the two-parameter Weibull density function (the trend of which is proportional to the two-parameter Weibull probability density function). The trend function (conditioned and non-conditioned) is analyzed to obtain fits and forecasts for a real data set, taking into account the mean value of the process, the maximum likelihood estimators of the parameters of the model and the computational problems that may arise. To carry out the task, we employ the simulated annealing method for finding the estimators values and achieve the study. Finally, to evaluate the capacity of the model, the study is applied to real modeling data where we discuss the accuracy according to error measures.
