robustbase

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 213 articles )

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  1. Bergström, Per; Edlund, Ove: Robust registration of surfaces using a refined iterative closest point algorithm with a trust region approach (2017)
  2. Koller, Manuel; Stahel, Werner A.: Nonsingular subsampling for regression S estimators with categorical predictors (2017)
  3. 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)
  4. Bako, Laurent; Ohlsson, Henrik: Analysis of a nonsmooth optimization approach to robust estimation (2016)
  5. Chen, Ting-Li; Fujisawa, Hironori; Huang, Su-Yun; Hwang, Chii-Ruey: On the weak convergence and central limit theorem of blurring and nonblurring processes with application to robust location estimation (2016)
  6. Dürre, Alexander; Vogel, Daniel: Asymptotics of the two-stage spatial sign correlation (2016)
  7. Ferraz do Nascimento, Fernando; Gamerman, Dani; Davis, Richard: A Bayesian semi-parametric approach to extreme regime identification (2016)
  8. García-Pérez, A.: A von Mises approximation to the small sample distribution of the trimmed mean (2016)
  9. Kato, Shogo; Eguchi, Shinto: Robust estimation of location and concentration parameters for the von Mises-Fisher distribution (2016)
  10. Kosiorowski, Daniel: Dilemmas of robust analysis of economic data streams (2016)
  11. Liski, Eero; Nordhausen, Klaus; Oja, Hannu; Ruiz-Gazen, Anne: Combining linear dimension reduction subspaces (2016)
  12. Prendergast, Luke A.; Healey, Alan F.: Improving estimated sufficient summary plots in dimension reduction using minimization criteria based on initial estimates (2016)
  13. Schmitt, Eric; Vakili, Kaveh: The FastHCS algorithm for robust PCA (2016)
  14. Strohriegl, Katharina; Hable, Robert: Qualitative robustness of estimators on stochastic processes (2016)
  15. Tsou, Tsung-Shan: Robust likelihood inference for multivariate correlated count data (2016)
  16. Wilcox, Rand R.: ANCOVA: a heteroscedastic global test when there is curvature and two covariates (2016)
  17. Withers, Christopher S.; Nadarajah, Saralees: $M$-estimators for regression with changing scale (2016)
  18. Yuan, Ke-Hai; Chan, Wai; Tian, Yubin: Expectation-robust algorithm and estimating equations for means and dispersion matrix with missing data (2016)
  19. Agostinelli, Claudio; Leung, Andy; Yohai, Victor J.; Zamar, Ruben H.: Rejoinder on: “Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination” (2015)
  20. Agostinelli, Claudio; Leung, Andy; Yohai, Victor J.; Zamar, Ruben H.: Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination (2015)

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