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

Citations

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Cited by:

  1. Bauer, Ida & Haupt, Harry & Linner, Stefan, 2024. "Pinball boosting of regression quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 200(C).
  2. Annika Strömer & Nadja Klein & Ingrid Van Keilegom & Andreas Mayr, 2025. "Modelling dependent censoring in time-to-event data using boosting copula regression," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 31(4), pages 994-1016, October.
  3. 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.
  4. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2025. "Electricity Sales and Forecasting of Stock Market Realized Volatility: A State-Level Analysis of the United States," Working Papers 202540, University of Pretoria, Department of Economics.
  5. 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.
  6. 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.
  7. 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.
  8. Heikki Kauppi, 2019. "Recession Prediction with OptimalUse of Leading Indicators," Discussion Papers 125, Aboa Centre for Economics.
  9. 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.
  10. 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.
  11. Michael Balzer & Elisabeth Bergherr & Swen Hutter & Tobias Hepp, 2026. "Gradient boosting for Dirichlet regression models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 110(1), pages 149-189, March.
  12. Tino Werner, 2025. "Loss-guided stability selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 19(1), pages 5-30, March.
  13. Michael Balzer & Adhen Benlahlou, 2025. "Mitigating consequences of prestige in citations of publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(11), pages 6035-6062, November.
  14. Juan Torres Munguía, 2024. "Identifying Gender-Specific Risk Factors for Income Poverty across Poverty Levels in Urban Mexico: A Model-Based Boosting Approach," Social Sciences, MDPI, vol. 13(3), pages 1-21, March.
  15. 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).
  16. Harald Binder & Hans Kestler & Matthias Schmid, 2014. "Proceedings of Reisensburg 2011," Computational Statistics, Springer, vol. 29(1), pages 1-2, February.
  17. Chung Shing Rex Ha & Martina Müller-Nurasyid & Agnese Petrera & Stefanie M Hauck & Federico Marini & Detlef K Bartsch & Emily P Slater & Konstantin Strauch, 2023. "Proteomics biomarker discovery for individualized prevention of familial pancreatic cancer using statistical learning," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-21, January.
  18. Katariina Pärnänen & Matti O. Ruuskanen & Guilhem Sommeria-Klein & Ville Laitinen & Pyry Kantanen & Guillaume Méric & Camila Gazolla Volpiano & Michael Inouye & Rob Knight & Veikko Salomaa & Aki S. Ha, 2025. "Variation and prognostic potential of the gut antibiotic resistome in the FINRISK 2002 cohort," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
  19. 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.
  20. Alina Schenk & Moritz Berger & Matthias Schmid, 2024. "Pseudo-value regression trees," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(2), pages 439-471, April.
  21. 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.
  22. Juan Armando Torres Munguía, 2024. "A model-based boosting approach to risk factors for physical intimate partner violence against women and girls in Mexico," Journal of Computational Social Science, Springer, vol. 7(2), pages 1937-1963, October.
  23. Thomas Welchowski & Matthias Schmid, 2019. "Sparse kernel deep stacking networks," Computational Statistics, Springer, vol. 34(3), pages 993-1014, September.
  24. 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.
  25. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
  26. Michael Balzer, 2026. "Fast and scalable variable selection for spatial autoregressive models," Statistical Papers, Springer, vol. 67(2), pages 1-35, April.
  27. 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.
  28. 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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