R package robustbase: Basic Robust Statistics. ”Essential” Robust Statistics. The goal is to provide tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics where we strive to cover the book ”Robust Statistics, Theory and Methods” by Maronna, Martin and Yohai; Wiley 2006.

References in zbMATH (referenced in 322 articles )

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  1. Aaron, Catherine; Cholaquidis, Alejandro; Fraiman, Ricardo; Ghattas, Badih: Multivariate and functional robust fusion methods for structured big data (2019)
  2. Agostinelli, Claudio; Valdora, Marina; Yohai, Victor J.: Initial robust estimation in generalized linear models (2019)
  3. Akbari, Mohammad Ghasem; Hesamian, Gholamreza: A partial-robust-ridge-based regression model with fuzzy predictors-responses (2019)
  4. Alvarez, Agustín; Boente, Graciela; Kudraszow, Nadia: Robust sieve estimators for functional canonical correlation analysis (2019)
  5. Cevallos-Valdiviezo, Holger; Van Aelst, Stefan: Fast computation of robust subspace estimators (2019)
  6. David Smith; Malcolm Faddy: Mean and Variance Modeling of Under-Dispersed and Over-Dispersed Grouped Binary Data (2019) not zbMATH
  7. Kapoor, Sayash; Patel, Kumar Kshitij; Kar, Purushottam: Corruption-tolerant bandit learning (2019)
  8. Martínez-Hernández, Israel; Genton, Marc G.; González-Farías, Graciela: Robust depth-based estimation of the functional autoregressive model (2019)
  9. Ortobelli, Sergio; Kouaissah, Noureddine; Tichý, Tomáš: On the use of conditional expectation in portfolio selection problems (2019)
  10. Slawski, Martin; Ben-David, Emanuel: Linear regression with sparsely permuted data (2019)
  11. Archimbaud, Aurore: Unsupervised outlier detection in quality control: an overview (2018)
  12. Archimbaud, Aurore; Nordhausen, Klaus; Ruiz-Gazen, Anne: ICS for multivariate outlier detection with application to quality control (2018)
  13. Bako, Laurent: Robustness analysis of a maximum correntropy framework for linear regression (2018)
  14. Brown, Jonathon D.: Advanced statistics for the behavioral sciences. A computational approach with R (2018)
  15. Filzmoser, Peter; Kurnaz, Fatma Sevinç: A robust Liu regression estimator (2018)
  16. Fontanari, Andrea; Cirillo, Pasquale; Oosterlee, Cornelis W.: From concentration profiles to concentration maps. New tools for the study of loss distributions (2018)
  17. Gimenez, Yanina; Giussani, Guido: Searching for the core variables in principal components analysis (2018)
  18. Goryainov, A. V.; Goryainov, V. B.: M-estimates of autoregression with random coefficients (2018)
  19. Kanno, Yoshihiro: Simple heuristic for data-driven computational elasticity with material data involving noise and outliers: a local robust regression approach (2018)
  20. Li, Meng; Schwartzman, Armin: Standardization of multivariate Gaussian mixture models and background adjustment of PET images in brain oncology (2018)

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