R package RobStatTM: Robust Statistics: Theory and Methods. Companion package for the book: ”Robust Statistics: Theory and Methods, second edition”, <>. This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.

References in zbMATH (referenced in 21 articles , 1 standard article )

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  1. Mishra, Aditya; Müller, Christian L.: Robust regression with compositional covariates (2022)
  2. Nordhausen, Klaus; Ruiz-Gazen, Anne: On the usage of joint diagonalization in multivariate statistics (2022)
  3. Salini, S.; Laurini, F.; Morelli, G.; Riani, M.; Cerioli, A.: Covariance matrices of S robust regression estimators (2022)
  4. Thompson, Ryan: Robust subset selection (2022)
  5. Zhao, Jun; Yan, Guan’ao; Zhang, Yi: Robust estimation and shrinkage in ultrahigh dimensional expectile regression with heavy tails and variance heterogeneity (2022)
  6. Axt, I.; Dürre, Alexander; Fried, Roland: Robust scale estimation under shifts in the mean (2021)
  7. Guerard, John B. Jr.; Xu, Ganlin; Markowitz, Harry: A further analysis of robust regression modeling and data mining corrections testing in global stocks (2021)
  8. Maronna, Ricardo A.: Robust functional principal components for irregularly spaced longitudinal data (2021)
  9. Navarro-Esteban, P.; Cuesta-Albertos, J. A.: High-dimensional outlier detection using random projections (2021)
  10. Poudyal, Chudamani: Truncated, censored, and actuarial payment-type moments for robust fitting of a single-parameter Pareto distribution (2021)
  11. Singh, Pushpinder; Mandal, Abhijit; Basu, Ayanendranath: Robust inference using the exponential-polynomial divergence (2021)
  12. Toka, Onur; Çetin, Meral; Arslan, Olcay: Robust regression estimation and variable selection when cellwise and casewise outliers are present (2021)
  13. Bianco, Ana M.; Boente, Graciela; González-Manteiga, Wenceslao; Pérez-González, Ana: Robust location estimators in regression models with covariates and responses missing at random (2020)
  14. 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)
  15. Boente, Graciela; Salibian-Barrera, Matías; Vena, Pablo: Robust estimation for semi-functional linear regression models (2020)
  16. Filzmoser, P.; Höppner, S.; Ortner, I.; Serneels, S.; Verdonck, T.: Cellwise robust M regression (2020)
  17. Valdora, Marina; Yohai, Víctor: M estimators based on the probability integral transformation with applications to count data (2020)
  18. Vidnerová, Petra; Kalina, Jan; Güney, Yeşim: A comparison of robust model choice criteria within a metalearning study (2020)
  19. Galeano, Pedro; Peña, Daniel: Data science, big data and statistics (2019)
  20. Maronna, Ricardo A.; Martin, R. Douglas; Yohai, Victor J.; Salibián-Barrera, Matías: Robust statistics. Theory and methods (with R) (2019)

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