IDEAS home Printed from https://ideas.repec.org/r/jss/jstsof/v070i04.html
   My bibliography  Save this item

bartMachine: Machine Learning with Bayesian Additive Regression Trees

Citations

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


Cited by:

  1. Pathairat Pastpipatkul & Htwe Ko, 2025. "Buddhist Thought on Happiness and Income Growth Relations Across Varying Income Countries," Journal of Happiness Studies, Springer, vol. 26(6), pages 1-24, August.
  2. Jan Kluge & Sarah Lappöhn & Kerstin Plank, 2023. "Predictors of TFP growth in European countries," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 50(1), pages 109-140, February.
  3. Ganguly, Prasangsha & Mukherjee, Sayanti, 2021. "A multifaceted risk assessment approach using statistical learning to evaluate socio-environmental factors associated with regional felony and misdemeanor rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
  4. Chanmin Kim & Mauricio Tec & Corwin Zigler, 2023. "Bayesian nonparametric adjustment of confounding," Biometrics, The International Biometric Society, vol. 79(4), pages 3252-3265, December.
  5. Falco J. Bargagli-Stoffi & Fabio Incerti & Massimo Riccaboni & Armando Rungi, 2023. "Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values," Papers 2306.08165, arXiv.org.
  6. Lihua Lei & Emmanuel J. Candès, 2021. "Conformal inference of counterfactuals and individual treatment effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 911-938, November.
  7. Benjamin Küfner & Joseph W. Sakshaug & Stefan Zins, 2022. "Analysing establishment survey non‐response using administrative data and machine learning," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 310-342, December.
  8. Pathairat Pastpipatkul & Terdthiti Chitkasame, 2025. "Analyzing the Impact of Carbon Mitigation on the Eurozone’s Trade Dynamics with the US and China," Econometrics, MDPI, vol. 13(3), pages 1-18, July.
  9. Alpha Forna & Ilaria Dorigatti & Pierre Nouvellet & Christl A Donnelly, 2021. "Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-15, September.
  10. Maia, Mateus & Murphy, Keefe & Parnell, Andrew C., 2024. "GP-BART: A novel Bayesian additive regression trees approach using Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
  11. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "Are precious metals a hedge against exchange-rate movements? An empirical exploration using bayesian additive regression trees," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 27-38.
  12. Ruijin Lu & Boya Zhang & Anna Birukov & Cuilin Zhang & Zhen Chen, 2024. "A Variance-Based Sensitivity Analysis Approach for Identifying Interactive Exposures," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 520-541, July.
  13. Kristina Blennow & Johannes Persson, 2021. "To Mitigate or Adapt? Explaining Why Citizens Responding to Climate Change Favour the Former," Land, MDPI, vol. 10(3), pages 1-13, March.
  14. Pierdzioch, Christian & Risse, Marian & Gupta, Rangan & Nyakabawo, Wendy, 2019. "On REIT returns and (un-)expected inflation: Empirical evidence based on Bayesian additive regression trees," Finance Research Letters, Elsevier, vol. 30(C), pages 160-169.
  15. Michael H Schwartz & Andrew J Ries & Andrew G Georgiadis & Hans Kainz, 2024. "Demonstrating the utility of Instrumented Gait Analysis in the treatment of children with cerebral palsy," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-23, April.
  16. Silvia Coderoni & Roberto Esposti & Alessandro Varacca, 2024. "How Differently Do Farms Respond to Agri-environmental Policies? A Probabilistic Machine-Learning Approach," Land Economics, University of Wisconsin Press, vol. 100(2), pages 370-397.
  17. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.
  18. Huaiyu Zang & Hang J. Kim & Bin Huang & Rhonda Szczesniak, 2023. "Bayesian causal inference for observational studies with missingness in covariates and outcomes," Biometrics, The International Biometric Society, vol. 79(4), pages 3624-3636, December.
  19. Lamprinakou, Stamatina & Barahona, Mauricio & Flaxman, Seth & Filippi, Sarah & Gandy, Axel & McCoy, Emma J., 2023. "BART-based inference for Poisson processes," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
  20. Kristina Blennow & Erik Persson & Johannes Persson, 2021. "DeveLoP—A Rationale and Toolbox for Democratic Landscape Planning," Sustainability, MDPI, vol. 13(21), pages 1-20, November.
  21. Jenny Häggström, 2018. "Data†driven confounder selection via Markov and Bayesian networks," Biometrics, The International Biometric Society, vol. 74(2), pages 389-398, June.
  22. Martin Huber & David Imhof & Rieko Ishii, 2022. "Transnational machine learning with screens for flagging bid‐rigging cartels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1074-1114, July.
  23. Dehghani, Nariman L. & Zamanian, Soroush & Shafieezadeh, Abdollah, 2021. "Adaptive network reliability analysis: Methodology and applications to power grid," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
  24. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
  25. Kluge, Jan & Lappoehn, Sarah & Plank, Kerstin, 2020. "The Determinants of Economic Competitiveness," IHS Working Paper Series 24, Institute for Advanced Studies.
  26. Bryan Keller, 2020. "Variable Selection for Causal Effect Estimation: Nonparametric Conditional Independence Testing With Random Forests," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 119-142, April.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.