On computational analysis of nonlinear regression models addressing heteroscedasticity and autocorrelation issues: An application to COVID-19 data

This paper develops a method for nonlinear regression models estimation that is robust to heteroscedasticity and autocorrelation of errors. Using nonlinear least squares estimation, four popular growth models (Exponential, Gompertz, Verhulst, and Weibull) were computed. Some assumptions on the error...

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Detalles Bibliográficos
Autores principales: Atchadé, Mintodê Nicodème, Tchanati P., Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9568860/
https://www.ncbi.nlm.nih.gov/pubmed/36254279
http://dx.doi.org/10.1016/j.heliyon.2022.e11057

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9568860/
https://www.ncbi.nlm.nih.gov/pubmed/36254279
http://dx.doi.org/10.1016/j.heliyon.2022.e11057

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