RESEARCH ARTICLE
Application of Quantile Regression: Modeling Body Mass Index in Ethiopia
Ashenafi Argaw Yirga1, Dawit Getnet Ayele2, *, Sileshi Fanta Melesse1
Article Information
Identifiers and Pagination:
Year: 2018Volume: 11
First Page: 221
Last Page: 233
Publisher ID: TOPHJ-11-221
DOI: 10.2174/1874944501811010221
Article History:
Received Date: 26/3/2018Revision Received Date: 10/5/2018
Acceptance Date: 15/5/2018
Electronic publication date: 31/5/2018
Collection year: 2018
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
Child malnutrition is the leading public health problem in developing countries. It is a major cause of child morbidity and mortality. Under-five children are the most vulnerable group for malnutrition. Body Mass Index (BMI) is a measure of nutritional status and is defined as the ratio of weight (kg) to squared height (m2). Studying the determinants of under-five children’s BMI is an important issue that needs to be addressed. This study applies quantile regression to study the determinants of under-five children BMI in Ethiopia.
Methods:
The weight-for-height index measures BMI. Quantiles are a generalization of percentiles for continuous random variables. Because it makes no distributional assumption about the error term in the model, quantile regression offers considerable model robustness.
Results:
The findings using quantile regression at different quantile levels were presented. The estimates across quantile levels were also performed. The findings of this study identified that for children under the age of five, the current age of mother, mother’s BMI, region (SNNPR and Somali) and weight of the child at birth (average and large) were found to be significantly affecting under-five children’s BMI across quantile levels.
Conclusion:
Quantile regression allows us to study the impact of predictors on different quantiles of the response distribution, and thus provides a complete picture of the relationship between the dependent and explanatory variables.
