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Bayesian Quantile Regression Method to Construct the Low Birth Weight Model

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 fo...

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Bibliographic Details
Published in:Journal of physics. Conference series 2019-08, Vol.1245 (1), p.12044
Main Authors: Yanuar, Ferra, Zetra, Aidinil, Muharisa, Catrin, Devianto, Dodi, Putri, Arrival Rince, Asdi, Yudiantri
Format: Article
Language:English
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1245/1/012044