References in zbMATH (referenced in 57 articles , 1 standard article )

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  1. Algeri, Sara; van Dyk, David A.: Testing one hypothesis multiple times (2021)
  2. James, Gareth; Witten, Daniela; Hastie, Trevor; Tibshirani, Robert: An introduction to statistical learning. With applications in R (2021)
  3. Ju, Xiaomeng; Salibián-Barrera, Matías: Robust boosting for regression problems (2021)
  4. Kalogridis, Ioannis; Van Aelst, Stefan: (M)-type penalized splines with auxiliary scale estimation (2021)
  5. Kowal, Marek; Skobel, Marcin; Gramacki, Artur; Korbicz, Józef: Breast cancer nuclei segmentation and classification based on a deep learning approach (2021)
  6. Pessa, Arthur A. B.; Ribeiro, Haroldo V.: ordpy: a Python package for data analysis with permutation entropy and ordinal network methods (2021)
  7. Bedoui, Adel; Lazar, Nicole A.: Bayesian empirical likelihood for ridge and Lasso regressions (2020)
  8. Gabrielli, Andrea: A neural network boosted double overdispersed Poisson claims reserving model (2020)
  9. Han, Sunwoo; Kim, Hyunjoong; Lee, Yung-Seop: Double random forest (2020)
  10. Huang, Liwen: Modified hybrid discriminant analysis methods and their applications in machine learning (2020)
  11. Kuwajima, Hiroshi; Yasuoka, Hirotoshi; Nakae, Toshihiro: Engineering problems in machine learning systems (2020)
  12. Rydén, Jesper: On features of fugue subjects. A comparison of J. S. Bach and later composers (2020)
  13. Salloum, Maher; Karlson, Kyle N.; Jin, Helena; Brown, Judith A.; Bolintineanu, Dan S.; Long, Kevin N.: Comparing field data using Alpert multi-wavelets (2020)
  14. Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli: DriveML: An R Package for Driverless Machine Learning (2020) arXiv
  15. Shan, Qianqian; Hong, Yili; Meeker, William Q.: Seasonal warranty prediction based on recurrent event data (2020)
  16. 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)
  17. Vencálek, Ondřej; Demni, Houyem; Messaoud, Amor; Porzio, Giovanni C.: On the optimality of the max-depth and max-rank classifiers for spherical data. (2020)
  18. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  19. Baltazar-Larios, F.; Esparza, Luz Judith R.: Bayesian estimation for the Markov-modulated diffusion risk model (2019)
  20. Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon: Lasso meets horseshoe: a survey (2019)

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