IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0265877.html
   My bibliography  Save this article

Application of quantile regression to examine changes in the distribution of Height for Age (HAZ) of Indian children aged 0–36 months using four rounds of NFHS data

Author

Listed:
  • Thirupathi Reddy Mokalla
  • Vishnu Vardhana Rao Mendu

Abstract

Background: The prevalence of stunting among under- three Indian children though decreased, still it is considered to be alarmingly high. In most of the previous studies, traditional (linear and logistic) regression analyses were applied. They were limited to encapsulated cross-distribution variations. The objective of the current study was to examine how the different determinants were heterogeneous in various percentiles of height for age (HAZ) distribution. Methods and findings: This article examined the change in the HAZ distribution of children and examined the relationships between the key co-variate trends and patterns in HAZ among children aged

Suggested Citation

  • Thirupathi Reddy Mokalla & Vishnu Vardhana Rao Mendu, 2022. "Application of quantile regression to examine changes in the distribution of Height for Age (HAZ) of Indian children aged 0–36 months using four rounds of NFHS data," PLOS ONE, Public Library of Science, vol. 17(5), pages 1-22, May.
    • Handle: RePEc:plo:pone00:0265877
    DOI: 10.1371/journal.pone.0265877
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0265877
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0265877&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0265877?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Corsi, Daniel J. & Mejía-Guevara, Iván & Subramanian, S.V., 2016. "Risk factors for chronic undernutrition among children in India: Estimating relative importance, population attributable risk and fractions," Social Science & Medicine, Elsevier, vol. 157(C), pages 165-185.
    2. Léandre Bassole, 2007. "Child Malnutrition in Senegal : Does access to public infrastructure really matter ? A quantile regression analysis"," Post-Print hal-00159503, HAL.
    3. Rana Khan & Muhammad Raza, 2016. "Determinants of malnutrition in Indian children: new evidence from IDHS through CIAF," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 299-316, January.
    4. Fenske, Nora & Kneib, Thomas & Hothorn, Torsten, 2011. "Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 494-510.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kim, Rockli & Rajpal, Sunil & Joe, William & Corsi, Daniel J. & Sankar, Rajan & Kumar, Alok & Subramanian, S.V., 2019. "Assessing associational strength of 23 correlates of child anthropometric failure: An econometric analysis of the 2015-2016 National Family Health Survey, India," Social Science & Medicine, Elsevier, vol. 238(C), pages 1-1.
    2. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    3. Benjamin Hofner & Andreas Mayr & Nikolay Robinzonov & Matthias Schmid, 2014. "Model-based boosting in R: a hands-on tutorial using the R package mboost," Computational Statistics, Springer, vol. 29(1), pages 3-35, February.
    4. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    5. Bonato, Matteo & Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian, 2018. "Gold futures returns and realized moments: A forecasting experiment using a quantile-boosting approach," Resources Policy, Elsevier, vol. 57(C), pages 196-212.
    6. Hofner, Benjamin & Mayr, Andreas & Schmid, Matthias, 2016. "gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i01).
    7. Shyamkumar Sriram & Lubna Naz, 2025. "Inequality of opportunity in child nutrition in Pakistan," PLOS ONE, Public Library of Science, vol. 20(2), pages 1-19, February.
    8. Agarwal, Sandip K. & Mishra, Shubham, 2024. "Health impact evaluation of Aspirational Districts Program in India: Evidence from National Family Health Survey," Economics & Human Biology, Elsevier, vol. 54(C).
    9. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers 70/13, Institute for Fiscal Studies.
    10. Zhao, Weihua & Lian, Heng & Song, Xinyuan, 2017. "Composite quantile regression for correlated data," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 15-33.
    11. Katsushi S. Imai & Samuel Kobina Annim & Veena S. Kulkarni & Raghav Gaiha, 2012. "Nutrition, Activity Intensity and Wage Linkages: Evidence from India," Discussion Paper Series DP2012-10, Research Institute for Economics & Business Administration, Kobe University, revised May 2014.
    12. Alexander März & Nadja Klein & Thomas Kneib & Oliver Musshoff, 2016. "Analysing farmland rental rates using Bayesian geoadditive quantile regression," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(4), pages 663-698.
    13. Mohamed Ouhourane & Yi Yang & Andréa L. Benedet & Karim Oualkacha, 2022. "Group penalized quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 495-529, September.
    14. Juan Armando Torres Munguía & Inmaculada Martínez-Zarzoso, 2021. "Examining gender inequalities in factors associated with income poverty in Mexican rural households," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-25, November.
    15. Noh, Hohsuk & Lee, Eun, 2012. "Component Selection in Additive Quantile Regression Models," LIDAM Discussion Papers ISBA 2012021, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    16. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A quantile-boosting approach to forecasting gold returns," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 38-55.
    17. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
    18. Tepegjozova Marija & Zhou Jing & Claeskens Gerda & Czado Claudia, 2022. "Nonparametric C- and D-vine-based quantile regression," Dependence Modeling, De Gruyter, vol. 10(1), pages 1-21, January.
    19. Demirer, Riza & Pierdzioch, Christian & Zhang, Huacheng, 2017. "On the short-term predictability of stock returns: A quantile boosting approach," Finance Research Letters, Elsevier, vol. 22(C), pages 35-41.
    20. Md Merajul Islam & Nobab Md Shoukot Jahan Kibria & Sujit Kumar & Dulal Chandra Roy & Md Rezaul Karim, 2024. "Prediction of undernutrition and identification of its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-22, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0265877. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.