robustbase

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 369 articles )

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  1. Bagdonavičius, Vilijandas; Petkevičius, Linas: A new multiple outliers identification method in linear regression (2020)
  2. Bianco, Ana M.; Boente, Graciela; Rodrigues, Isabel M.: Robust Wald-type methods for testing equality between two populations regression parameters: a comparative study under the logistic model (2020)
  3. Boente, Graciela; Rodriguez, Daniela; Vena, Pablo: Robust estimators in a generalized partly linear regression model under monotony constraints (2020)
  4. Boudt, Kris; Rousseeuw, Peter J.; Vanduffel, Steven; Verdonck, Tim: The minimum regularized covariance determinant estimator (2020)
  5. Filzmoser, P.; Höppner, S.; Ortner, I.; Serneels, S.; Verdonck, T.: Cellwise robust M regression (2020)
  6. Hesamian, Gholamreza; Akbari, Mohammad Ghasem: A robust varying coefficient approach to fuzzy multiple regression model (2020)
  7. Lu, Kang-Ping; Chang, Shao-Tung: Robust algorithms for multiphase regression models (2020)
  8. Song, Junmo: Robust test for dispersion parameter change in discretely observed diffusion processes (2020)
  9. Valdora, Marina; Yohai, Víctor: M estimators based on the probability integral transformation with applications to count data (2020)
  10. Aaron, Catherine; Cholaquidis, Alejandro; Fraiman, Ricardo; Ghattas, Badih: Multivariate and functional robust fusion methods for structured big data (2019)
  11. Agostinelli, Claudio; Valdora, Marina; Yohai, Victor J.: Initial robust estimation in generalized linear models (2019)
  12. Akbari, Mohammad Ghasem; Hesamian, Gholamreza: A partial-robust-ridge-based regression model with fuzzy predictors-responses (2019)
  13. Alvarez, Agustín; Boente, Graciela; Kudraszow, Nadia: Robust sieve estimators for functional canonical correlation analysis (2019)
  14. Bianco, Ana M.; Spano, Paula M.: Robust inference for nonlinear regression models (2019)
  15. Cevallos-Valdiviezo, Holger; Van Aelst, Stefan: Fast computation of robust subspace estimators (2019)
  16. David Smith; Malcolm Faddy: Mean and Variance Modeling of Under-Dispersed and Over-Dispersed Grouped Binary Data (2019) not zbMATH
  17. Debruyne, Michiel; Höppner, Sebastiaan; Serneels, Sven; Verdonck, Tim: Outlyingness: which variables contribute most? (2019)
  18. Freue, Gabriela V. Cohen; Kepplinger, David; Salibián-Barrera, Matías; Smucler, Ezequiel: Robust elastic net estimators for variable selection and identification of proteomic biomarkers (2019)
  19. Galeano, Pedro; Peña, Daniel: Data science, big data and statistics (2019)
  20. Godichon-Baggioni, Antoine: Online estimation of the asymptotic variance for averaged stochastic gradient algorithms (2019)

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