Quantile Estimation Using the Log-Skew-Normal Linear Regression Model with Application to Children’s Weight Data

In this article, we establish properties that relate quantiles of the log-skew-normal distribution to its parameters, allowing us to investigate the relationship between quantiles of a positive skewed response variable and a set of explanatory variables via the log-skew-normal linear regression model. We compute the maximum likelihood estimates of the parameters through a correspondence between the log-skew-normal and skew-normal linear regression models. Monte Carlo simulations show the satisfactory performance of the quantile estimators. An application to children’s data is presented and discussed.

• Autores: Morán Vázquez, Raúl Alejandro; Giraldo Melo, Anlly Daniela; Mazo Lopera, Mauricio Alejandro
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2023
• Institución: Universidad de Antioquia
• Repositorio: Repositorio UdeA
• Idioma: eng
• OAI Identifier: oai:bibliotecadigital.udea.edu.co:10495/46080
• Acceso en línea: https://hdl.handle.net/10495/46080
• Palabra clave: Análisis de Regresión; Regression Analysis; Modelo matemático; Mathematical models; Distribución normal; Normal distribution; Skew-normal distribution; http://aims.fao.org/aos/agrovoc/c_24199; http://aims.fao.org/aos/agrovoc/c_4ed623ba; https://id.nlm.nih.gov/mesh/D012044; ODS 3: Salud y bienestar. Garantizar una vida sana y promover el bienestar de todos a todas las edades
• Rights: openAccess
• License: http://creativecommons.org/licenses/by/4.0/