Inference on diffusion processes related to a general growth model Albano, Giuseppina Barrera, Antonio Giorno, Virginia Torres Ruiz, Francisco De Asís Lognormal process Gaussian Processes Estimation Moth-flame optimization algorithm This research is partially supported by PID2020-1187879GB-100 and CEX2020-001105-M grants, funded by MCIN/AEI/10.13039/501100011033 (Spain). The authors G. Albano and V. Giorno acknowledge support received from the “ European Union – Next Generation EU” through MUR-PRIN 2022, project 2022XZSAFN “Anomalous Phenomena on Regular and Irregular Domains: Approximating Complexity for the Applied Sciences,” and MUR-PRIN 2022 PNRR, project P2022XSF5H “ Stochastic Models in Biomathematics and Applications.”. They are members of the GNCS-INdAM. Funding for open access charge: University of Granada / CBUA. 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. 2025-03-11T08:32:20Z 2025-03-11T08:32:20Z 2025-02-20 journal article Albano, G., Barrera, A., Giorno, V. et al. Inference on diffusion processes related to a general growth model. Stat Comput 35, 52 (2025). https://doi.org/10.1007/s11222-025-10562-5 https://hdl.handle.net/10481/102980 10.1007/s11222-025-10562-5 eng info:eu-repo/grantAgreement/EC/Next GenerationEU/2022XZSAFN info:eu-repo/grantAgreement/EC/Next GenerationEU/P2022XSF5H http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Springer Nature