R: Optimally robust estimation

ROptEst-package {ROptEst}

R Documentation

Optimally robust estimation

Description

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

Details

Package:	ROptEst
Version:	1.3.3
Date:	2024-02-07
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:	1286

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 peter.ruckdeschel@uni-oldenburg.de,
Matthias Kohl Matthias.Kohl@stamats.de
Maintainer: Matthias Kohl matthias.kohl@stamats.de

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. M. Kohl, P. Ruckdeschel, and H. Rieder (2010). Infinitesimally Robust Estimation in General Smoothly Parametrized Models. Statistical Methods and Applications 19(3): 333-354. doi:10.1007/s10260-010-0133-0. H. Rieder (1994): Robust Asymptotic Statistics. Springer. 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. doi:10.1007/s10260-007-0047-7 P. Ruckdeschel (2005). Optimally One-Sided Bounded Influence Curves. Mathematical Methods of Statistics 14(1), 105-131. P. Ruckdeschel and H. Rieder (2004). Optimal Influence Curves for General Loss Functions. Statistics & Decisions 22, 201-223. doi:10.1524/stnd.22.3.201.57067

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())

[Package ROptEst version 1.3.3 Index]