The project RobASt aims for the implementation of R packages for the computation of optimally robust estimators and tests as well as the necessary infrastructure (mainly S4 classes and methods) and diagnostics; cf. M. Kohl (2005).
Keywords for this software
References in zbMATH (referenced in 9 articles , 1 standard article )
Showing results 1 to 9 of 9.
- Shariati, Nima; Shahriari, Hamid; Shafaei, Rasoul: Parameter estimation of autoregressive models using the iteratively robust filtered fast-$\tau$ method (2014)
- Ruckdeschel, Peter; Horbenko, Nataliya: Optimally robust estimators in generalized Pareto models (2013)
- Kohl, Matthias: Bounded influence estimation for regression and scale (2012)
- Hable, Robert: Minimum distance estimation in imprecise probability models (2010)
- Hable, R.; Ruckdeschel, P.; Rieder, H.: Optimal robust influence functions in semiparametric regression (2010)
- Kohl, Matthias; Ruckdeschel, Peter; Rieder, Helmut: Infinitesimally robust estimation in general smoothly parametrized models (2010)
- Shevlyakov, Georgy; Lee, Jae Won; Lee, Kyung Min; Shin, Vladimir; Kim, Kiseon: Robust detection of a weak signal with redescending $M$-estimators: a comparative study (2010)
- Rieder, Helmut; Kohl, Matthias; Ruckdeschel, Peter: The cost of not knowing the radius (2008)
- Kohl, Matthias: Numerical contributions to the asymptotic theory of robustness. With CD-ROM. (2005)