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Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression

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  • Fenske, Nora
  • Kneib, Thomas
  • Hothorn, Torsten

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  • 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.
  • Handle: RePEc:bes:jnlasa:v:106:i:494:y:2011:p:494-510
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    File URL: http://pubs.amstat.org/doi/abs/10.1198/jasa.2011.ap09272
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    Citations

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    Cited by:

    1. Elisabeth Waldmann & Thomas Kneib & Yu Ryan Yu & Stefan Lang, 2012. "Bayesian semiparametric additive quantile regression," Working Papers 2012-06, Faculty of Economics and Statistics, University of Innsbruck.
    2. repec:eee:finlet:v:22:y:2017:i:c:p:35-41 is not listed on IDEAS
    3. Alexander März & Nadja Klein & Thomas Kneib & Oliver Musshoff, 2016. "Analysing farmland rental rates using Bayesian geoadditive quantile regression," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 43(4), pages 663-698.
    4. 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.
    5. Matteo Bonato & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2016. "Gold Futures Returns and Realized Moments: A Forecasting Experiment Using a Quantile-Boosting Approach," Working Papers 201645, University of Pretoria, Department of Economics.
    6. repec:jss:jstsof:v:074:i01 is not listed on IDEAS
    7. Yaeji Lim & Hee-Seok Oh, 2015. "Simultaneous confidence interval for quantile regression," Computational Statistics, Springer, vol. 30(2), pages 345-358, June.
    8. Fenske Nora & Fahrmeir Ludwig & Hothorn Torsten & Rzehak Peter & Höhle Michael, 2013. "Boosting Structured Additive Quantile Regression for Longitudinal Childhood Obesity Data," The International Journal of Biostatistics, De Gruyter, vol. 9(1), pages 1-18, July.
    9. 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.
    10. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Papers 1312.7186, arXiv.org, revised Jun 2016.
    11. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP70/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Zhao, Weihua & Lian, Heng & Song, Xinyuan, 2017. "Composite quantile regression for correlated data," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 15-33.

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