Two-Parameter Stochastic Weibull Diffusion Model: Statistical Inference and Application to Real Modeling Example
Metadatos
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MDPI
Materia
Weibull distribution Stochastic diffusion process Likelihood estimation Age dependency ratio
Fecha
2020-01-23Referencia bibliográfica
Nafidi, A., Bahij, M., Gutiérrez-Sánchez, R., & Achchab, B. (2020). Two-Parameter Stochastic Weibull Diffusion Model: Statistical Inference and Application to Real Modeling Example. Mathematics, 8(2), 160.
Patrocinador
This reasearch was financed by LAMSAD from “Fonds propres de l’Université Hassan I” (Morocco) and FQM-147 from “Plan Andaluz de l’Investigaciòn” (Spain).Resumen
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.