Bayesian Quantile Regression Method to Construct the Low Birth Weight Model

Yanuar, Ferra; Zetra, Aidinil; Muharisa, Catrin; Devianto, Dodi; Putri, Arrival Rince; Asdi, Yudiantri

doi:10.1088/1742-6596/1245/1/012044

Cited by 8 publications

(9 citation statements)

References 11 publications

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“…They then suggested implementing the Bayesian approach for constructing the model with a small to moderate size sample. Bayesian techniques for variable selection in quantile regression have received considerable attention in recent literature because Bayesian methods are often more competitive for small or moderate data sets with a low signal-to-noise ratio [16][17][18]. Li et al [19] gave a generic treatment to a set of regularization approaches, including Lasso, group Lasso, and net elastic penalties.…”

Section: Introductionmentioning

confidence: 99%

Modified Quantile Regression for Modeling the Low Birth Weight

Yanuar

Yozza

Zetra

2022

Front. Appl. Math. Stat.

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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.

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Section: Introductionmentioning

confidence: 99%

Modified Quantile Regression for Modeling the Low Birth Weight

Yanuar

Yozza

Zetra

2022

Front. Appl. Math. Stat.

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“…As pointed out already, 1 penalty term in (2) could be interpreated as a Bayesian posterior mode estimated under independent Laplace priors for the regression coefficient Bayesian approach to quantile regression method is in combination the minimizing problem in (2). Asymmetric Laplace error distribution is used to maximize likelihood distribution as equivalent way in minimizing the equation [10], [11], [14], [15], [16]:…”

Section: Letmentioning

confidence: 99%

Bayesian LASSO Quantile Regression: An Application to the Modeling of Low Birth Weight

Yanuar¹,

Zetra²,

Putri³

et al. 2020

Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bog

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The modeling of low birth weight using ordinary least square is not appropriate and inefficient. The low birth weight data violates the normality assumption since the data is right skewed. The data usually contains outliers as well. Many researchers used quantile regression approach to model this case but this method has limitation. The limitation of this approach is need moderate to big sample size. This study aims to combine the quantile regression with Bayesian LASSO approach to model the low birth weight. Bayesian method has ability to model small sample size since it involves the information related to data (known as likelihood function) and prior information about the parameter tobe estimated (prior distribution). This study demonstrated that Bayesian quantile regression and Bayesian LASSO (Least Absolute Shrinkage Selection Operator)quantile regression could yield the acceptable model of low birth weight case based on indicators of goodness of fit model. Bayesian LASSO quantile regression produced better estimated parameter values since it yielded shorter 95% Bayesian credible interval than Bayesian quantile regression.

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“…Oh et al do selecting variables using the Bayesian quantile regression method using the Savage-Dickey density ratio [14]. Furthermore, the application of Bayesian quantile regression was also applied in constructing a low birth weight model using the Gibbs Sampling algorithm approach [15].…”

Section: Introductionmentioning

confidence: 99%

Modeling Length of Hospital Stay for Patients With COVID-19 in West Sumatra Using Quantile Regression Approach

Yanuar

Deva

Maiyastri

et al. 2021

CAUCHY

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This study aims to construct the model for the length of hospital stay for patients with COVID-19 using quantile regression and Bayesian quantile approaches. The quantile regression models the relationship at any point of the conditional distribution of the dependent variable on several independent variables. The Bayesian quantile regression combines the concept of quantile analysis into the Bayesian approach. In the Bayesian approach, the Asymmetric Laplace Distribution (ALD) distribution is used to form the likelihood function as the basis for formulating the posterior distribution. All 688 patients with COVID-19 treated in M. Djamil Hospital and Universitas Andalas Hospital in Padang City between March-July 2020 were used in this study. This study found that the Bayesian quantile regression method results in a smaller 95% confidence interval and higher value than the quantile regression method. It is concluded that the Bayesian quantile regression method tends to yield a better model than the quantile method. Based on the Bayesian quantile regression method, it investigates that the length of hospital stay for patients with COVID-19 in West Sumatra is significantly influenced by Age, Diagnoses status, and Discharge status.

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Bayesian Quantile Regression Method to Construct the Low Birth Weight Model

Cited by 8 publications

References 11 publications

Modified Quantile Regression for Modeling the Low Birth Weight

Modified Quantile Regression for Modeling the Low Birth Weight

Bayesian LASSO Quantile Regression: An Application to the Modeling of Low Birth Weight

Modeling Length of Hospital Stay for Patients With COVID-19 in West Sumatra Using Quantile Regression Approach

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