IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0189677.html
   My bibliography  Save this article

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis

Author

Listed:
  • Alamgir Kabir
  • Md Jahanur Rahman
  • Abu Ahmed Shamim
  • Rolf D W Klemm
  • Alain B Labrique
  • Mahbubur Rashid
  • Parul Christian
  • Keith P West Jr.

Abstract

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p

Suggested Citation

  • Alamgir Kabir & Md Jahanur Rahman & Abu Ahmed Shamim & Rolf D W Klemm & Alain B Labrique & Mahbubur Rashid & Parul Christian & Keith P West Jr., 2017. "Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-16, December.
    • Handle: RePEc:plo:pone00:0189677
    DOI: 10.1371/journal.pone.0189677
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189677
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0189677&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0189677?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. Mevik, Björn-Helge & Wehrens, Ron, 2007. "The pls Package: Principal Component and Partial Least Squares Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 18(i02).
    2. Ian T. Jolliffe, 1982. "A Note on the Use of Principal Components in Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(3), pages 300-303, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shifaw, Eshetu & Sha, Jinming & Li, Xiaomei & Bao, Zhongcong & Zhou, Zhenglong, 2019. "An insight into land-cover changes and their impacts on ecosystem services before and after the implementation of a comprehensive experimental zone plan in Pingtan island, China," Land Use Policy, Elsevier, vol. 82(C), pages 631-642.
    2. Eshetu Shifaw & Jinming Sha & Xiaomei Li & Shang Jiali & Zhongcong Bao, 2020. "Remote sensing and GIS-based analysis of urban dynamics and modelling of its drivers, the case of Pingtan, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(3), pages 2159-2186, March.

    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. Tanin Sirimongkolkasem & Reza Drikvandi, 2019. "On Regularisation Methods for Analysis of High Dimensional Data," Annals of Data Science, Springer, vol. 6(4), pages 737-763, December.
    2. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    3. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    4. Carlos Moreno-Miranda & Hipatia Palacios & Daniele Rama, 2019. "Small-holders perception of sustainability and chain coordination: evidence from Arriba PDO Cocoa in Western Ecuador," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 8(3), December.
    5. repec:jss:jstsof:23:i12 is not listed on IDEAS
    6. Fernandez-Haddad, Zaira & Quiroga, Sonia, 2011. "Adaptation Of Mediterranean Crops To Water Pressure In The Ebro Basin: A Water Efficiency Index," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114358, European Association of Agricultural Economists.
    7. Kawano, Shuichi & Fujisawa, Hironori & Takada, Toyoyuki & Shiroishi, Toshihiko, 2015. "Sparse principal component regression with adaptive loading," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 192-203.
    8. Heni Masruroh & Soemarno Soemarno & Syahrul Kurniawan & Amin Setyo Leksono, 2023. "A Spatial Model of Landslides with A Micro-Topography and Vegetation Approach for Sustainable Land Management in the Volcanic Area," Sustainability, MDPI, vol. 15(4), pages 1-26, February.
    9. Elton Mammadov & Michael Denk & Frank Riedel & Cezary Kaźmierowski & Karolina Lewinska & Remigiusz Łukowiak & Witold Grzebisz & Amrakh I. Mamedov & Cornelia Glaesser, 2022. "Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains," Land, MDPI, vol. 11(3), pages 1-24, March.
    10. Giacomo Crucil & Fabio Castaldi & Emilien Aldana-Jague & Bas van Wesemael & Andy Macdonald & Kristof Van Oost, 2019. "Assessing the Performance of UAS-Compatible Multispectral and Hyperspectral Sensors for Soil Organic Carbon Prediction," Sustainability, MDPI, vol. 11(7), pages 1-18, March.
    11. Minjung Kyung & Ju-Hyun Park & Ji Yeh Choi, 2022. "Bayesian Mixture Model of Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 946-966, September.
    12. Hugh L. Christensen, 2015. "Algorithmic arbitrage of open-end funds using variational Bayes," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(04), pages 1-38, December.
    13. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
    14. Mirza Pasic & Halima Hadziahmetovic & Ismira Ahmovic & Mugdim Pasic, 2023. "Principal Component Regression Modeling and Analysis of PM 10 and Meteorological Parameters in Sarajevo with and without Temperature Inversion," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    15. Cai, Yuezhou & Hanley, Aoife, 2012. "Building BRICS: 2-Stage DEA analysis of R&D efficiency," Kiel Working Papers 1788, Kiel Institute for the World Economy (IfW Kiel).
    16. Travaglini, Guido, 2010. "Supervised Principal Components and Factor Instrumental Variables. An Application to Violent CrimeTrends in the US, 1982-2005," MPRA Paper 22077, University Library of Munich, Germany.
    17. Bennett, Donyetta & Mekelburg, Erik & Strauss, Jack & Williams, T.H., 2024. "Unlocking the black box of sentiment and cryptocurrency: What, which, why, when and how?," Global Finance Journal, Elsevier, vol. 60(C).
    18. Alessandro Barbarino & Efstathia Bura, 2015. "Forecasting with Sufficient Dimension Reductions," Finance and Economics Discussion Series 2015-74, Board of Governors of the Federal Reserve System (U.S.).
    19. Elkin Castaño & Santiago Gallón, 2017. "A solution for multicollinearity in stochastic frontier production function models," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 86, pages 9-23, Enero - J.
    20. Mansouri, Majdi & Hajji, Mansour & Trabelsi, Mohamed & Harkat, Mohamed Faouzi & Al-khazraji, Ayman & Livera, Andreas & Nounou, Hazem & Nounou, Mohamed, 2018. "An effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test," Energy, Elsevier, vol. 159(C), pages 842-856.
    21. Ranjith Vijayakumar & Ji Yeh Choi & Eun Hwa Jung, 2022. "A Unified Neural Network Framework for Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1503-1528, 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:pone00:0189677. 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.