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 407 articles )

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  1. Agostinelli, Claudio; Bianco, Ana M.; Boente, Graciela: Robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter (2020)
  2. Bagdonavičius, Vilijandas; Petkevičius, Linas: A new multiple outliers identification method in linear regression (2020)
  3. Beyhum, Jad: Inference robust to outliers with (\ell_1)-norm penalization (2020)
  4. 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)
  5. Boente, Graciela; Rodriguez, Daniela; Vena, Pablo: Robust estimators in a generalized partly linear regression model under monotony constraints (2020)
  6. Boudt, Kris; Rousseeuw, Peter J.; Vanduffel, Steven; Verdonck, Tim: The minimum regularized covariance determinant estimator (2020)
  7. Chakraborty, Arnab; Lahiri, Soumendra Nath; Wilson, Alyson: A statistical analysis of noisy crowdsourced weather data (2020)
  8. Fauß, Michael; Zoubir, Abdelhak M.; Poor, H. Vincent: Minimax optimal sequential hypothesis tests for Markov processes (2020)
  9. Feng, Yunlong; Wu, Qiang: Learning under ((1 + \epsilon))-moment conditions (2020)
  10. Filzmoser, Peter; Gregorich, Mariella: Multivariate outlier detection in applied data analysis: global, local, compositional and cellwise outliers (2020)
  11. Filzmoser, P.; Höppner, S.; Ortner, I.; Serneels, S.; Verdonck, T.: Cellwise robust M regression (2020)
  12. Gerstenberger, Carina; Vogel, Daniel; Wendler, Martin: Tests for scale changes based on pairwise differences (2020)
  13. Hesamian, Gholamreza; Akbari, Mohammad Ghasem: A robust varying coefficient approach to fuzzy multiple regression model (2020)
  14. Laurent, Sébastien; Shi, Shuping: Volatility estimation and jump detection for drift-diffusion processes (2020)
  15. Lu, Kang-Ping; Chang, Shao-Tung: Robust algorithms for multiphase regression models (2020)
  16. Song, Junmo: Robust test for dispersion parameter change in discretely observed diffusion processes (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. Zhao, Qingyuan; Wang, Jingshu; Hemani, Gibran; Bowden, Jack; Small, Dylan S.: Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2020)
  20. Aaron, Catherine; Cholaquidis, Alejandro; Fraiman, Ricardo; Ghattas, Badih: Multivariate and functional robust fusion methods for structured big data (2019)

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