R package RobStatTM: Robust Statistics: Theory and Methods. Companion package for the book: ”Robust Statistics: Theory and Methods, second edition”, <http://www.wiley.com/go/maronna/robust>. 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.
Keywords for this software
References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
- Poudyal, Chudamani: Truncated, censored, and actuarial payment-type moments for robust fitting of a single-parameter Pareto distribution (2021)
- 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)
- Filzmoser, P.; Höppner, S.; Ortner, I.; Serneels, S.; Verdonck, T.: Cellwise robust M regression (2020)
- Valdora, Marina; Yohai, Víctor: M estimators based on the probability integral transformation with applications to count data (2020)
- Vidnerová, Petra; Kalina, Jan; Güney, Yeşim: A comparison of robust model choice criteria within a metalearning study (2020)
- Galeano, Pedro; Peña, Daniel: Data science, big data and statistics (2019)
- Maronna, Ricardo A.; Martin, R. Douglas; Yohai, Victor J.; Salibián-Barrera, Matías: Robust statistics. Theory and methods (with R) (2019)
- Nagy, Stanislav; Schütt, Carsten; Werner, Elisabeth M.: Halfspace depth and floating body (2019)