0RobLox-package: Optimally robust influence curves and estimators for location...

In RobLox: Optimally Robust Influence Curves and Estimators for Location and Scale

Optimally robust influence curves and estimators for location and scale

Description

Functions for the determination of optimally robust influence curves and estimators in case of normal location and/or scale (see Chapter 8 in Kohl (2005) <https://epub.uni-bayreuth.de/839/2/DissMKohl.pdf>).

Package versions

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.

Author(s)

Matthias Kohl [e-mail]

References

M. Kohl (2005). Numerical Contributions to the Asymptotic Theory of Robustness. Dissertation. University of Bayreuth. https://epub.uni-bayreuth.de/id/eprint/839/2/DissMKohl.pdf.

H. Rieder (1994): Robust Asymptotic Statistics. Springer. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-1-4684-0624-5")}

H. Rieder, M. Kohl, and P. Ruckdeschel (2008). The Costs of Not Knowing the Radius. Statistical Methods and Applications 17(1): 13-40. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10260-007-0047-7")} M. Kohl, P. Ruckdeschel, and H. Rieder (2010). Infinitesimally Robust Estimation in General Smoothly Parametrized Models. Statistical Methods and Applications 19(3): 333-354. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10260-010-0133-0")}.

M. Kohl and H.P. Deigner (2010). Preprocessing of gene expression data by optimally robust estimators. BMC Bioinformatics 11, 583. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/1471-2105-11-583")}.

M. Kohl (2012). Bounded influence estimation for regression and scale. Statistics, 46(4): 437-488. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/02331888.2010.540668")}

See Also

RobAStBase-package

Examples

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)

RobLox documentation built on April 3, 2025, 5:50 p.m.