0ROptEst-package: Optimally robust estimation

In ROptEst: Optimally Robust Estimation

Optimally robust estimation

Description

Optimally robust estimation in general smoothly parameterized models using S4 classes and methods.

Details

Package:	ROptEst
Version:	1.3.1
Date:	2022-11-16
Depends:	R(>= 3.4), methods, distr(>= 2.8.0), distrEx(>= 2.8.0), distrMod(>= 2.8.1),RandVar(>= 1.2.0), RobAStBase(>= 1.2.0)
Suggests:	RobLox
Imports:	startupmsg, MASS, stats, graphics, utils, grDevices
ByteCompile:	yes
Encoding:	latin1
License:	LGPL-3
URL:	https://robast.r-forge.r-project.org/
VCS/SVNRevision:	1238

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)

Peter Ruckdeschel [e-mail],
Matthias Kohl [e-mail]
Maintainer: Matthias Kohl [e-mail]

References

M. Kohl (2005). Numerical Contributions to the Asymptotic Theory of Robustness. Dissertation. University of Bayreuth. M. Kohl, P. Ruckdeschel, H. Rieder (2010). Infinitesimally Robust Estimation in General Smoothly Parametrized Models. Statistical Methods and Application 19(3):333-354.

See Also

distr-package, distrEx-package, distrMod-package, RandVar-package, RobAStBase-package

Examples

## don't test to reduce check time on CRAN

library(ROptEst)
## Example: Rutherford-Geiger (1910); cf. Feller~(1968), Section VI.7 (a)
x <- c(rep(0, 57), rep(1, 203), rep(2, 383), rep(3, 525), rep(4, 532), 
       rep(5, 408), rep(6, 273), rep(7, 139), rep(8, 45), rep(9, 27), 
       rep(10, 10), rep(11, 4), rep(12, 0), rep(13, 1), rep(14, 1))
## ML-estimate from package distrMod
MLest <- MLEstimator(x, PoisFamily())
MLest
## confidence interval based on CLT
confint(MLest)
## compute optimally (w.r.t to MSE) robust estimator (unknown contamination)
robEst <- roptest(x, PoisFamily(), eps.upper = 0.1, steps = 3)
estimate(robEst)
## check influence curve
pIC(robEst)
checkIC(pIC(robEst))
## plot influence curve
plot(pIC(robEst))
## confidence interval based on LAN - neglecting bias
confint(robEst)
## confidence interval based on LAN - including bias
confint(robEst, method = symmetricBias())

ROptEst documentation built on Nov. 17, 2022, 1:06 a.m.