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

Raúl Alejandro Morán-Vásquez (), Anlly Daniela Giraldo-Melo and Mauricio A. Mazo-Lopera
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Raúl Alejandro Morán-Vásquez: Instituto de Matemáticas, Universidad de Antioquia, Calle 67 No. 53-108, Medellín 050010, Colombia
Anlly Daniela Giraldo-Melo: Instituto de Matemáticas, Universidad de Antioquia, Calle 67 No. 53-108, Medellín 050010, Colombia
Mauricio A. Mazo-Lopera: Escuela de Estadística, Universidad Nacional de Colombia, Carrera 65 No. 59A-110, Medellín 050034, Colombia

Mathematics, 2023, vol. 11, issue 17, 1-10

Abstract: 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.

Keywords: child growth standards; non-normal regression models; quantile modeling; skew-normal distribution (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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