Modified Quantile Regression for Modeling the Low Birth Weight

• Published in: Frontiers in Applied Mathematics and Statistics
• Main Authors: Ferra Yanuar, Hazmira Yozza, Aidinil Zetra
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2022-06-01
• Subjects: low birth weight; Bayesian quantile regression; Bayesian Lasso quantile regression; Bayesian Adaptive Lasso quantile regression; quantile regression
• Online Access: https://www.frontiersin.org/articles/10.3389/fams.2022.890028/full

This study aims to identify the best model of low birth weight by applying and comparing several methods based on the quantile regression method's modification. The birth weight data is violated with linear model assumptions; thus, quantile approaches are used. The quantile regression is adjusted by combining it with the Bayesian approach since the Bayesian method can produce the best model in small size samples. Three kinds of the modified quantile regression methods considered here are the Bayesian quantile regression, the Bayesian Lasso quantile regression, and the Bayesian Adaptive Lasso quantile regression. This article implements the skewed Laplace distribution as the likelihood function in Bayesian analysis. The cross-sectional study collected the primary data of 150 birth weights in West Sumatera, Indonesia. This study indicated that Bayesian Adaptive Lasso quantile regression performed well compared to the other two methods based on a smaller absolute bias and a shorter Bayesian credible interval based on the simulation study. This study also found that the best model of birth weight is significantly affected by maternal education, the number of pregnancy problems, and parity.