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Model-based boosting in R: a hands-on tutorial using the R package mboost

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  • Benjamin Hofner
  • Andreas Mayr
  • Nikolay Robinzonov
  • Matthias Schmid

Abstract

We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates as base-learners for fitting various kinds of generalized linear and generalized additive models to potentially high-dimensional data. We give a theoretical background and demonstrate how mboost can be used to fit interpretable models of different complexity. As an example we use mboost to predict the body fat based on anthropometric measurements throughout the tutorial. Copyright Springer-Verlag Berlin Heidelberg 2014

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  • 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.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:1:p:3-35
    DOI: 10.1007/s00180-012-0382-5
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    References listed on IDEAS

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    1. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, January.
    2. Thomas Kneib & Torsten Hothorn & Gerhard Tutz, 2009. "Variable Selection and Model Choice in Geoadditive Regression Models," Biometrics, The International Biometric Society, vol. 65(2), pages 626-634, June.
    3. 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.
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    1. Robert Suchting & Michael S. Businelle & Stephen W. Hwang & Nikhil S. Padhye & Yijiong Yang & Diane M. Santa Maria, 2020. "Predicting Daily Sheltering Arrangements among Youth Experiencing Homelessness Using Diary Measurements Collected by Ecological Momentary Assessment," IJERPH, MDPI, vol. 17(18), pages 1-17, September.
    2. Guilherme Lindenmeyer & Pedro Pablo Skorin & Hudson da Silva Torrent, 2021. "Using boosting for forecasting electric energy consumption during a recession: a case study for the Brazilian State Rio Grande do Sul," Letters in Spatial and Resource Sciences, Springer, vol. 14(2), pages 111-128, August.
    3. 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.
    4. Riccardo De Bin & Vegard Grødem Stikbakke, 2023. "A boosting first-hitting-time model for survival analysis in high-dimensional settings," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 420-440, April.
    5. Heikki Kauppi, 2019. "Recession Prediction with OptimalUse of Leading Indicators," Discussion Papers 125, Aboa Centre for Economics.
    6. Lahiri, Kajal & Yang, Cheng, 2022. "Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York," International Journal of Forecasting, Elsevier, vol. 38(2), pages 545-566.
    7. Ngandu Balekelayi & Solomon Tesfamariam, 2020. "Geoadditive Quantile Regression Model for Sewer Pipes Deterioration Using Boosting Optimization Algorithm," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    8. Citores, L. & Ibaibarriaga, L. & Lee, D.-J. & Brewer, M.J. & Santos, M. & Chust, G., 2020. "Modelling species presence–absence in the ecological niche theory framework using shape-constrained generalized additive models," Ecological Modelling, Elsevier, vol. 418(C).
    9. Harald Binder & Hans Kestler & Matthias Schmid, 2014. "Proceedings of Reisensburg 2011," Computational Statistics, Springer, vol. 29(1), pages 1-2, February.
    10. Riccardo De Bin, 2016. "Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost," Computational Statistics, Springer, vol. 31(2), pages 513-531, June.
    11. Boyao Zhang & Tobias Hepp & Sonja Greven & Elisabeth Bergherr, 2022. "Adaptive step-length selection in gradient boosting for Gaussian location and scale models," Computational Statistics, Springer, vol. 37(5), pages 2295-2332, November.
    12. Thomas Welchowski & Matthias Schmid, 2019. "Sparse kernel deep stacking networks," Computational Statistics, Springer, vol. 34(3), pages 993-1014, September.
    13. Mai Dao & Min Wang & Souparno Ghosh & Keying Ye, 2022. "Bayesian variable selection and estimation in quantile regression using a quantile-specific prior," Computational Statistics, Springer, vol. 37(3), pages 1339-1368, July.
    14. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
    15. Philipp F. M. Baumann & Enzo Rossi & Alexander Volkmann, 2020. "What Drives Inflation and How: Evidence from Additive Mixed Models Selected by cAIC," Papers 2006.06274, arXiv.org, revised Aug 2022.
    16. Heidi Seibold & Christoph Bernau & Anne-Laure Boulesteix & Riccardo De Bin, 2018. "On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models," Computational Statistics, Springer, vol. 33(3), pages 1195-1215, September.

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