Optimally robust influence curves and estimators for location and scale
Functions for the determination of optimally robust influence curves and estimators in case of normal location and/or scale.
| Package: | RobLox |
| Version: | 1.2.0 |
| Date: | 2019-04-02 |
| Depends: | R(>= 3.4), stats, distrMod(>= 2.8.0), RobAStBase(>= 1.2.0) |
| Imports: | methods, lattice, RColorBrewer, Biobase, RandVar(>= 1.2.0), distr(>= 2.8.0) |
| Suggests: | MASS |
| ByteCompile: | yes |
| License: | LGPL-3 |
| URL: | http://robast.r-forge.r-project.org/ |
| VCS/SVNRevision: | 1214 |
Note: The first two numbers of package versions do not necessarily reflect package-individual development, but rather are chosen for the RobAStXXX family as a whole in order to ease updating "depends" information.
Matthias Kohl matthias.kohl@stamats.de
M. Kohl (2005). Numerical Contributions to the Asymptotic Theory of Robustness. Dissertation. University of Bayreuth. Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer. Rieder, H., Kohl, M. and Ruckdeschel, P. (2008). The Costs of not Knowing the Radius. Statistical Methods and Applications 17(1) 13-40. Extended version: http://r-kurs.de/RRlong.pdf
M. Kohl, P. Ruckdeschel, and H. Rieder (2010). Infinitesimally Robust Estimation in General Smoothly Parametrized Models. Statistical Methods and Application, 19(3):333-354.
library(RobLox) ind <- rbinom(100, size=1, prob=0.05) x <- rnorm(100, mean=ind*3, sd=(1-ind) + ind*9) roblox(x) res <- roblox(x, eps.lower = 0.01, eps.upper = 0.1, returnIC = TRUE) estimate(res) confint(res) confint(res, method = symmetricBias()) pIC(res) ## don't run to reduce check time on CRAN ## Not run: checkIC(pIC(res)) Risks(pIC(res)) Infos(pIC(res)) plot(pIC(res)) infoPlot(pIC(res)) ## End(Not run) ## row-wise application ind <- rbinom(200, size=1, prob=0.05) X <- matrix(rnorm(200, mean=ind*3, sd=(1-ind) + ind*9), nrow = 2) rowRoblox(X)
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