Inference on diffusion processes related to a general growth model

Abstract This paper considers two stochastic diffusion processes associated with a general growth curve that includes a wide family of growth phenomena. The resulting processes are lognormal and Gaussian, and for them inference is addressed by means of the maximum likelihood method. The complexity of the resulting system of equations requires the use of metaheuristic techniques. The limitation of the parameter space, typically required by all metaheuristic techniques, is also provided by means of a suitable strategy. Several simulation studies are performed to evaluate to goodness of the proposed methodology, and an application to real data is described.

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Publication Details

Journal
Statistics and Computing
Published
2025-02-20
DOI
https://doi.org/10.1007/s11222-025-10562-5
Primary Topic
Advanced Mathematical Modeling in Engineering
Type
article
Field-Weighted Citation Impact
0.00

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article

Inference on diffusion processes related to a general growth model

Antonio Barrera, Virginia Giorno, Giuseppina Albano, Francisco Torres‐Ruiz
Statistics and Computing
Advanced Mathematical Modeling in Engineering
article

Inference on diffusion processes related to a general growth model

Antonio Barrera, Virginia Giorno, Giuseppina Albano, Francisco Torres‐Ruiz
article en

Abstract

Abstract This paper considers two stochastic diffusion processes associated with a general growth curve that includes a wide family of growth phenomena. The resulting processes are lognormal and Gaussian, and for them inference is addressed by means of the maximum likelihood method. The complexity of the resulting system of equations requires the use of metaheuristic techniques. The limitation of the parameter space, typically required by all metaheuristic techniques, is also provided by means of a suitable strategy. Several simulation studies are performed to evaluate to goodness of the proposed methodology, and an application to real data is described.

Statistics and ComputingVol. 35(2)
University of Salerno (IT), Universidad de Granada (ES), Universidad de Málaga (ES)
Istituto Nazionale di Alta Matematica "Francesco Severi", European Commission, Universidad de Granada, Gruppo Nazionale per il Calcolo Scientifico, Agencia Estatal de Investigación
Openalex Percentile: Top 96%
Advanced Mathematical Modeling in Engineering
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