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 429 articles )

Showing results 41 to 60 of 429.
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  1. Agostinelli, Claudio; Valdora, Marina; Yohai, Victor J.: Initial robust estimation in generalized linear models (2019)
  2. Akbari, Mohammad Ghasem; Hesamian, Gholamreza: A partial-robust-ridge-based regression model with fuzzy predictors-responses (2019)
  3. Alvarez, Agustín; Boente, Graciela; Kudraszow, Nadia: Robust sieve estimators for functional canonical correlation analysis (2019)
  4. Bianco, Ana M.; Spano, Paula M.: Robust inference for nonlinear regression models (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. Debruyne, Michiel; Höppner, Sebastiaan; Serneels, Sven; Verdonck, Tim: Outlyingness: which variables contribute most? (2019)
  8. Freue, Gabriela V. Cohen; Kepplinger, David; Salibián-Barrera, Matías; Smucler, Ezequiel: Robust elastic net estimators for variable selection and identification of proteomic biomarkers (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. Goryainov, V. B.; Goryainova, E. R.: Comparative analysis of robust and classical methods for estimating the parameters of a threshold autoregression equation (2019)
  12. Johansen, Søren; Nielsen, Bent: Boundedness of M-estimators for linear regression in time series (2019)
  13. Kalina, J.; Tichavský, J.: Statistical learning for recommending (robust) nonlinear regression methods (2019)
  14. Kapoor, Sayash; Patel, Kumar Kshitij; Kar, Purushottam: Corruption-tolerant bandit learning (2019)
  15. Kazi-Tani, Nabil; Rullière, Didier: On a construction of multivariate distributions given some multidimensional marginals (2019)
  16. Lee, Seokho; Kim, Seonhwa: Marginalized Lasso in sparse regression (2019)
  17. Li, Bin; Marx, Brian D.; Chakraborty, Somsubhra; Weindorf, David C.: Multivariate calibration with robust signal regression (2019)
  18. Lima, Italo R.; Cao, Guanqun; Billor, Nedret: M-based simultaneous inference for the mean function of functional data (2019)
  19. Marazzi, Alfio; Valdora, Marina; Yohai, Victor; Amiguet, Michael: A robust conditional maximum likelihood estimator for generalized linear models with a dispersion parameter (2019)
  20. Martínez-Hernández, Israel; Genton, Marc G.; González-Farías, Graciela: Robust depth-based estimation of the functional autoregressive model (2019)

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