Applying bootstrap quantile regression for the construction of a low birth weight model
| Main Authors: | Yanuar, Ferra, Yozza, Hazmira, Firdawati, Firdawati, Rahmi, Izzati, Zetra, Aidinil |
|---|---|
| Format: | Book application/pdf Journal |
| Terbitan: |
UI Scholars Hub
, 2019
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| Subjects: | |
| Online Access: |
https://scholarhub.ui.ac.id/mjhr/vol23/iss2/5 https://scholarhub.ui.ac.id/cgi/viewcontent.cgi?article=1004&context=mjhr |
| ctrlnum |
mjhr-1004 |
|---|---|
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title>Applying bootstrap quantile regression for the construction of a low birth weight model</title><creator>Yanuar, Ferra</creator><creator>Yozza, Hazmira</creator><creator>Firdawati, Firdawati</creator><creator>Rahmi, Izzati</creator><creator>Zetra, Aidinil</creator><description>Background: Most investigators use ordinary least squares (OLS) methods to model low birth weight. When the data are non-normal or contain outliers, OLS become ineffective. However, the quantile method of forecasting low birth weight has not been fully evaluated, although it has good potential for overcoming problems associated with linear regression. Methods: The present study reports our comparison between the OLS and quantile regression methods for modeling low birth weight when the data are right skewed and outliers are presented. Additionally, we evaluated the performance of the associated algorithm in recovering the true parameter using the bootstrap method. Results: Our study found that a mother’s education level, the number of maternal parities, and the last birth interval significantly impacted low birth weight at any selected low quantile. Based on the bootstrap simulation study, the proposed model was considered to be acceptable since both methods generated nearly identical estimates of the parameter model. An accuracy test proved that the quantile method was an unbiased estimator. Conclusions: The present study found that low birth weight is significantly affected by the mother’s educational level, the number of maternal parities, and the last birth interval.</description><date>2019-08-01T07:00:00Z</date><type>Book:Book</type><type>File:application/pdf</type><identifier>https://scholarhub.ui.ac.id/mjhr/vol23/iss2/5</identifier><identifier>https://scholarhub.ui.ac.id/cgi/viewcontent.cgi?article=1004&amp;context=mjhr</identifier><source>Makara Journal of Health Research</source><publisher>UI Scholars Hub</publisher><subject>bootstrap approach</subject><subject>low birth weight</subject><subject>quantile regression</subject><subject>Medicine and Health Sciences</subject><recordID>mjhr-1004</recordID></dc>
|
| format |
Book:Book Book File:application/pdf File Journal:Journal Journal |
| author |
Yanuar, Ferra Yozza, Hazmira Firdawati, Firdawati Rahmi, Izzati Zetra, Aidinil |
| title |
Applying bootstrap quantile regression for the construction of a low birth weight model |
| publisher |
UI Scholars Hub |
| publishDate |
2019 |
| topic |
bootstrap approach low birth weight quantile regression Medicine and Health Sciences |
| url |
https://scholarhub.ui.ac.id/mjhr/vol23/iss2/5 https://scholarhub.ui.ac.id/cgi/viewcontent.cgi?article=1004&context=mjhr |
| contents |
Background: Most investigators use ordinary least squares (OLS) methods to model low birth weight. When the data are non-normal or contain outliers, OLS become ineffective. However, the quantile method of forecasting low birth weight has not been fully evaluated, although it has good potential for overcoming problems associated with linear regression. Methods: The present study reports our comparison between the OLS and quantile regression methods for modeling low birth weight when the data are right skewed and outliers are presented. Additionally, we evaluated the performance of the associated algorithm in recovering the true parameter using the bootstrap method. Results: Our study found that a mother’s education level, the number of maternal parities, and the last birth interval significantly impacted low birth weight at any selected low quantile. Based on the bootstrap simulation study, the proposed model was considered to be acceptable since both methods generated nearly identical estimates of the parameter model. An accuracy test proved that the quantile method was an unbiased estimator. Conclusions: The present study found that low birth weight is significantly affected by the mother’s educational level, the number of maternal parities, and the last birth interval. |
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IOS17919.mjhr-1004 |
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Fakultas Kedokteran Universitas Indonesia |
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7109 |
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library:university library |
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Departemen Kimia Kedokteran |
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6027 |
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Indonesian Journal of Medical Chemistry and Bioinformatics |
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17919 |
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JAKARTA PUSAT |
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DKI JAKARTA |
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IOS17919 |
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2022-11-22T23:23:17Z |
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