References in zbMATH (referenced in 41 articles )

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  1. Brentnall, Adam R.; Cuzick, Jack: Risk models for breast cancer and their validation (2020)
  2. McManus, Scott; Rahman, Azizur; Horta, Ana; Coombes, Jacqueline: Applied Bayesian modeling for assessment of interpretation uncertainty in spatial domains (2020)
  3. Naka, Poontavika; Boado-Penas, María del Carmen; Lanot, Gauthier: A multiple state model for the working-age disabled population using cross-sectional data (2020)
  4. Orozco-Acosta, Erick; Llinás-Solano, Humberto; Fonseca-Rodríguez, Javier: Convergence theorems in multinomial saturated and logistic models (2020)
  5. Srinivasan, Shriram; Cawi, Eric; Hyman, Jeffrey; Osthus, Dave; Hagberg, Aric; Viswanathan, Hari; Srinivasan, Gowri: Physics-informed machine learning for backbone identification in discrete fracture networks (2020)
  6. Wang, Ximei; Hu, Min; Zhao, Yanlong; Djehiche, Boualem: Credit scoring based on the set-valued identification method (2020)
  7. Carpita, Maurizio; Ciavolino, Enrico; Pasca, Paola: Exploring and modelling team performances of the kaggle European soccer database (2019)
  8. Coskun, Burcin; Alpu, O.: Diagnostics of multiple group influential observations for logistic regression models (2019)
  9. Minary, Pauline; Pichon, Frédéric; Mercier, David; Lefevre, Eric; Droit, Benjamin: Evidential joint calibration of binary SVM classifiers (2019)
  10. Roy, Asim; Qureshi, Shiban; Pande, Kartikeya; Nair, Divitha; Gairola, Kartik; Jain, Pooja; Singh, Suraj; Sharma, Kirti; Jagadale, Akshay; Lin, Yi-Yang; Sharma, Shashank; Gotety, Ramya; Zhang, Yuexin; Tang, Ji; Mehta, Tejas; Sindhanuru, Hemanth; Okafor, Nonso; Das, Santak; Gopal, Chidambara N.; Rudraraju, Srinivasa B.; Kakarlapudi, Avinash V.: Performance comparison of machine learning platforms (2019)
  11. Schaeben, H.; Kost, S.; Semmler, G.: Popular raster-based methods of prospectivity modeling and their relationships (2019)
  12. Tapia, Alejandra; Leiva, Victor; Diaz, Maria del Pilar; Giampaoli, Viviana: Influence diagnostics in mixed effects logistic regression models (2019)
  13. Zarei, Shaho; Mohammadpour, Adel: Using synthetic data and dimensionality reduction in high-dimensional classification via logistic regression (2019)
  14. Zhang, Chun-Xia; Xu, Shuang; Zhang, Jiang-She: A novel variational Bayesian method for variable selection in logistic regression models (2019)
  15. Zhang, Zhiwang; He, Jing; Gao, Guangxia; Tian, Yingjie: Sparse multi-criteria optimization classifier for credit risk evaluation (2019)
  16. Zhao, Zijin; Chu, Lingyang; Tao, Dacheng; Pei, Jian: Classification with label noise: a Markov chain sampling framework (2019)
  17. Abdel Aal, Medhat M.; Saad Mowafy, Mamdouh A. Alim; Etman, Nehal: A statistical approach for understanding the plethora of miRNAs and mRNAs interplay (2018)
  18. Aduenko, Alexander A.; Motrenko, Anastasia P.; Strijov, Vadim V.: Object selection in credit scoring using covariance matrix of parameters estimations (2018)
  19. Aragon, Davi C.; Achcar, Jorge A.; Martinez, Edson Z.: Maximum likelihood and Bayesian estimators for the double Poisson distribution (2018)
  20. Bergtold, Jason S.; Pokharel, Krishna P.; Featherstone, Allen M.; Mo, Lijia: On the examination of the reliability of statistical software for estimating regression models with discrete dependent variables (2018)

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