References in zbMATH (referenced in 22 articles )

Showing results 1 to 20 of 22.
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  1. Bertsimas, Dimitris; Dunn, Jack; Wang, Yuchen: Near-optimal nonlinear regression trees (2021)
  2. Ertefaie, Ashkan; McKay, James R.; Oslin, David; Strawderman, Robert L.: Robust Q-learning (2021)
  3. Fitzpatrick, Trevor; Mues, Christophe: How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments (2021)
  4. Zhang, Yongli; Rolling, Craig; Yang, Yuhong: Estimating and forecasting dynamic correlation matrices: a nonlinear common factor approach (2021)
  5. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  6. Ribeiro, Rita P.; Moniz, Nuno: Imbalanced regression and extreme value prediction (2020)
  7. Sage, Andrew J.; Genschel, Ulrike; Nettleton, Dan: Tree aggregation for random forest class probability estimation (2020)
  8. Andreas Anastasiou, Piotr Fryzlewicz: Detecting multiple generalized change-points by isolating single ones (2019) arXiv
  9. Bagirov, Adil; Taheri, Sona; Asadi, Soodabeh: A difference of convex optimization algorithm for piecewise linear regression (2019)
  10. Cerqueira, Vitor; Torgo, Luís; Pinto, Fábio; Soares, Carlos: Arbitrage of forecasting experts (2019)
  11. de la Llave, Miguel Ángel; López, Fernando A.; Angulo, Ana: The impact of geographical factors on churn prediction: an application to an insurance company in Madrid’s urban area (2019)
  12. García Nieto, P. J.; García-Gonzalo, E.; Sánchez Lasheras, F.; Paredes-Sánchez, J. P.; Riesgo Fernández, P.: Forecast of the higher heating value in biomass torrefaction by means of machine learning techniques (2019)
  13. García Nieto, P. J.; García-Gonzalo, E.; Álvarez Antón, J. C.; González Suárez, V. M.; Mayo Bayón, R.; Mateos Martín, F.: A comparison of several machine learning techniques for the centerline segregation prediction in continuous cast steel slabs and evaluation of its performance (2018)
  14. Stoklosa, Jakub; Warton, David I.: A generalized estimating equation approach to multivariate adaptive regression splines (2018)
  15. Hart, J. L.; Alexanderian, A.; Gremaud, P. A.: Efficient computation of Sobol’ indices for stochastic models (2017)
  16. Duncan, I.; Loginov, M.; Ludkovski, M.: Testing alternative regression frameworks for predictive modeling of health care costs (2016)
  17. Yazıcı, Ceyda; Yerlikaya-Özkurt, Fatma; Batmaz, İnci: A computational approach to nonparametric regression: bootstrapping CMARS method (2015)
  18. Schnitzer, Mireille E.; Van der Laan, Mark J.; Moodie, Erica E. M.; Platt, Robert W.: Effect of breastfeeding on gastrointestinal infection in infants: a targeted maximum likelihood approach for clustered longitudinal data (2014)
  19. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  20. Karabatsos, George; Walker, Stephen G.: Adaptive-modal Bayesian nonparametric regression (2012)

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