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 346 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. Bianco, Ana M.; Spano, Paula M.: Robust inference for nonlinear regression models (2019)
  6. Cevallos-Valdiviezo, Holger; Van Aelst, Stefan: Fast computation of robust subspace estimators (2019)
  7. David Smith; Malcolm Faddy: Mean and Variance Modeling of Under-Dispersed and Over-Dispersed Grouped Binary Data (2019) not zbMATH
  8. Debruyne, Michiel; Höppner, Sebastiaan; Serneels, Sven; Verdonck, Tim: Outlyingness: which variables contribute most? (2019)
  9. Galeano, Pedro; Peña, Daniel: Data science, big data and statistics (2019)
  10. Godichon-Baggioni, Antoine: Online estimation of the asymptotic variance for averaged stochastic gradient algorithms (2019)
  11. Johansen, Søren; Nielsen, Bent: Boundedness of M-estimators for linear regression in time series (2019)
  12. Kapoor, Sayash; Patel, Kumar Kshitij; Kar, Purushottam: Corruption-tolerant bandit learning (2019)
  13. Kazi-Tani, Nabil; Rullière, Didier: On a construction of multivariate distributions given some multidimensional marginals (2019)
  14. Lee, Seokho; Kim, Seonhwa: Marginalized Lasso in sparse regression (2019)
  15. Lima, Italo R.; Cao, Guanqun; Billor, Nedret: M-based simultaneous inference for the mean function of functional data (2019)
  16. Marazzi, Alfio; Valdora, Marina; Yohai, Victor; Amiguet, Michael: A robust conditional maximum likelihood estimator for generalized linear models with a dispersion parameter (2019)
  17. Martínez-Hernández, Israel; Genton, Marc G.; González-Farías, Graciela: Robust depth-based estimation of the functional autoregressive model (2019)
  18. Mbah, Chamberlain; Peremans, Kris; Van Aelst, Stefan; Benoit, Dries F.: Robust Bayesian seemingly unrelated regression model (2019)
  19. Nagy, Stanislav; Schütt, Carsten; Werner, Elisabeth M.: Halfspace depth and floating body (2019)
  20. Nowak, Piotr Bolesław: Moment-type estimation from grouped samples (2019)

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