T-Growth Stochastic Model: Simulation and Inference via Metaheuristic Algorithms
Metadatos
Mostrar el registro completo del ítemEditorial
MDPI
Materia
Growth curves Diffusion processes Inference on diffusion processes Metaheuristic algorithms
Fecha
2021Referencia bibliográfica
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
Patrocinador
Ministerio 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-UGR18Resumen
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.