References in zbMATH (referenced in 52 articles , 1 standard article )

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  1. Ju, Xiaomeng; Salibián-Barrera, Matías: Robust boosting for regression problems (2021)
  2. Kalogridis, Ioannis; Van Aelst, Stefan: (M)-type penalized splines with auxiliary scale estimation (2021)
  3. Bedoui, Adel; Lazar, Nicole A.: Bayesian empirical likelihood for ridge and Lasso regressions (2020)
  4. Gabrielli, Andrea: A neural network boosted double overdispersed Poisson claims reserving model (2020)
  5. Han, Sunwoo; Kim, Hyunjoong; Lee, Yung-Seop: Double random forest (2020)
  6. Huang, Liwen: Modified hybrid discriminant analysis methods and their applications in machine learning (2020)
  7. Kuwajima, Hiroshi; Yasuoka, Hirotoshi; Nakae, Toshihiro: Engineering problems in machine learning systems (2020)
  8. Salloum, Maher; Karlson, Kyle N.; Jin, Helena; Brown, Judith A.; Bolintineanu, Dan S.; Long, Kevin N.: Comparing field data using Alpert multi-wavelets (2020)
  9. Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli: DriveML: An R Package for Driverless Machine Learning (2020) arXiv
  10. Shan, Qianqian; Hong, Yili; Meeker, William Q.: Seasonal warranty prediction based on recurrent event data (2020)
  11. Tuo, Rui; Wang, Yan; Jeff Wu, C. F.: On the improved rates of convergence for Matérn-type kernel ridge regression with application to calibration of computer models (2020)
  12. Vencálek, Ondřej; Demni, Houyem; Messaoud, Amor; Porzio, Giovanni C.: On the optimality of the max-depth and max-rank classifiers for spherical data. (2020)
  13. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  14. Baltazar-Larios, F.; Esparza, Luz Judith R.: Bayesian estimation for the Markov-modulated diffusion risk model (2019)
  15. Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon: Lasso meets horseshoe: a survey (2019)
  16. Chen, Li-Pang: Book review of: Mehryar Mohri et al., Foundations of machine learning. 2nd ed. (2019)
  17. Chen, Li-Pang; Yi, Grace Y.; Zhang, Qihuang; He, Wenqing: Multiclass analysis and prediction with network structured covariates (2019)
  18. Chen, Ying; Niu, Linlin; Chen, Ray-Bing; He, Qiang: Sparse-group independent component analysis with application to yield curves prediction (2019)
  19. Cossette, Hélène; Gadoury, Simon-Pierre; Marceau, Etienne; Robert, Christian Y.: Composite likelihood estimation method for hierarchical Archimedean copulas defined with multivariate compound distributions (2019)
  20. Huck, Nicolas: Large data sets and machine learning: applications to statistical arbitrage (2019)

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