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 265 articles )

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  1. Bako, Laurent: Robustness analysis of a maximum correntropy framework for linear regression (2018)
  2. Fontanari, Andrea; Cirillo, Pasquale; Oosterlee, Cornelis W.: From concentration profiles to concentration maps. New tools for the study of loss distributions (2018)
  3. Alih, Ekele; Ong, Hong Choon: Robust cluster-based multivariate outlier diagnostics and parameter estimation in regression analysis (2017)
  4. Atkinson, Anthony C.; Corbellini, Aldo; Riani, Marco: Robust Bayesian regression with the forward search: theory and data analysis (2017)
  5. Bergström, Per; Edlund, Ove: Robust registration of surfaces using a refined iterative closest point algorithm with a trust region approach (2017)
  6. Boente, Graciela; Martínez, Alejandra: Marginal integration $M$-estimators for additive models (2017)
  7. Boente, Graciela; Vahnovan, Alejandra: Robust estimators in semi-functional partial linear regression models (2017)
  8. Bun, Joël; Bouchaud, Jean-Philippe; Potters, Marc: Cleaning large correlation matrices: tools from random matrix theory (2017)
  9. Callegaro, Giorgia; Gaïgi, M’hamed; Scotti, Simone; Sgarra, Carlo: Optimal investment in markets with over and under-reaction to information (2017)
  10. Cardot, Hervé; Godichon-Baggioni, Antoine: Fast estimation of the median covariation matrix with application to online robust principal components analysis (2017)
  11. Chachi, Jalal; Roozbeh, Mahdi: A fuzzy robust regression approach applied to bedload transport data (2017)
  12. Goryainova, E.R.; Botvinkin, E.A.: Experimental and analytic comparison of the accuracy of different estimates of parameters in a linear regression model (2017)
  13. Koller, Manuel; Stahel, Werner A.: Nonsingular subsampling for regression S estimators with categorical predictors (2017)
  14. Leão, Jeremias; Leiva, Víctor; Saulo, Helton; Tomazella, Vera: Birnbaum-Saunders frailty regression models: diagnostics and application to medical data (2017)
  15. Marchetti, Stefano; Giusti, Caterina; Salvati, Nicola; Pratesi, Monica: Small area estimation based on M-quantile models in presence of outliers in auxiliary variables (2017)
  16. O’Keefe, Christine M.; Ayre, Tim; Lucie, Sebastien; Khan, Atikur R.; Song, Soomin; Kwon, Soonmin: Perturbed robust linear estimating equations for confidentiality protection in remote analysis (2017)
  17. Pérez, B.; Molina, Isabel; Thieler, A.; Fried, R.; Peña, D.: Fast and robust estimators of variance components in the nested error model (2017)
  18. Alexander Kowarik; Matthias Templ: Imputation with the R Package VIM (2016)
  19. Anita Thieler; Roland Fried; Jonathan Rathjens: RobPer: An R Package to Calculate Periodograms for Light Curves Based on Robust Regression (2016)
  20. Atkinson, Anthony C.; Corbellini, Aldo; Riani, Marco: Introducing prior information into the forward search for regression (2016)

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