T-Growth Stochastic Model: Simulation and Inference via Metaheuristic Algorithms
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Growth curvesDiffusion processesInference on diffusion processesMetaheuristic algorithms
Barrera, A.; Román-Román, P.; Torres-Ruiz, F. T-Growth Stochastic Model: Simulation and Inference via Metaheuristic Algorithms. Mathematics 2021, 9, 959. https:// doi.org/10.3390/math9090959
SponsorshipMinisterio de Economía, Industria y Competitividad, Spain, under Grant MTM2017-85568-P; FEDER/Junta de Andalucía-Consejería de Economía y Conocimiento, Spain, Grant A-FQM-456-UGR18
The main objective of this work is to introduce a stochastic model associated with the one described by the T-growth curve, which is in turn a modification of the logistic curve. By conveniently reformulating the T curve, it may be obtained as a solution to a linear differential equation. This greatly simplifies the mathematical treatment of the model and allows a diffusion process to be defined, which is derived from the non-homogeneous lognormal diffusion process, whose mean function is a T curve. This allows the phenomenon under study to be viewed in a dynamic way. In these pages, the distribution of the process is obtained, as are its main characteristics. The maximum likelihood estimation procedure is carried out by optimization via metaheuristic algorithms. Thanks to an exhaustive study of the curve, a strategy is obtained to bound the parametric space, which is a requirement for the application of various swarm-based metaheuristic algorithms. A simulation study is presented to show the validity of the bounding procedure and an example based on real data is provided.