Open AccessBayesian Quantile Regression Method to Construct the Low Birth Weight Model
Author(s) -
Ferra Yanuar,
Aidinil Zetra,
Catrin Muharisa,
Dodi Devianto,
Arrival Rince Putri,
Yudiantri Asdi
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1245/1/012044
Subject(s) - quantile regression , quantile , gibbs sampling , statistics , bayesian probability , mathematics , regression analysis , econometrics , bayesian linear regression , binomial regression , bayesian inference
This study aims to implement Bayesian quantile regression method in constructing the model of Low Birth Weight. The data of Low Birth Weight is violated of nonnormal assumption for error terms. This study considers quantile regression approach and use Gibbs sampling algorithm from Bayesian method for fitting the quantile regression model. This study explores the performance of the asymmetric Laplace distribution for working likelihood in posterior estimation process. This study also compare the result of variable selection in quantile regression and Bayesian quantile regression for Low Birth Weight model. This study. proved that Bayesan quantile method produced better model than just quantile approach. Bayesian quantile method proved that it can handle the nonnormal problem although using moderate size of data.