Quantile Estimation Based on the Log-Skew- t Linear Regression Model: Statistical Aspects, Simulations, and Applications

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: Departamento de Estadística, Universidad Nacional de Colombia, Carrera 65 No. 59A-110, Medellín 050034, Colombia

Stats, 2025, vol. 8, issue 3, 1-16

Abstract: We propose a robust linear regression model assuming a log-skew- t distribution for the response variable, with the aim of exploring the association between the covariates and the quantiles of a continuous and positive response variable under skewness and heavy tails. This model includes the log-skew-normal and log- t linear regression models as special cases. Our simulation studies indicate good performance of the quantile estimation approach and its outperformance relative to the classical quantile regression model. The practical applicability of our methodology is demonstrated through an analysis of two real datasets.

Keywords: quantile regression; robust regression; log-skew-t distribution; income data (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
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