confintRmx: Confidence Intervals for RMX Estimates

In stamats/rmx: Radius-Minimax Estimators

Confidence Intervals for RMX Estimates

Description

The function confint computes confindence interval for rmx estimates.

Usage

## S3 method for class 'rmx'
confint(object, parm, level = 0.95, method = "as", R = 9999, type = "all", ...)

Arguments

object	object of S3 class rmx.
parm	see confint. A specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.
level	see confint, the confidence level required.
method	see details.
R	number of bootstrap replicates.
type	A vector of character strings representing the type of intervals required. The value should be any subset of the values c("norm","basic", "stud", "perc", "bca") or simply "all" which will compute all five types of intervals; see also boot.ci.
...	further arguments passed through especially to rowRmx.

Details

The function is inspired by the respective function of the RobASt-family of packages.

In case of optimally-robust RMX estimators computed with function rmx (S3 class rmx), confint intervals are computed either only using the asymptotic (co)variance (method = "as", default) or using the asymptotic (co)variance and the maximum asymptotic bias (method = "as.bias") or using bootstrap (method = "boot").

Value

An object of class "rmxCI" is returned. It contails at least the following arguments:

method	method selected for computing the confidence interval.
conf.int	numeric matrix with the confidence bounds.
rmxEst	point estimates.

Author(s)

Matthias Kohl [e-mail]

References

Kohl, M. (2005). Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.

M. Kohl, P. Ruckdeschel, and H. Rieder (2010). Infinitesimally Robust Estimation in General Smoothly Parametrized Models. Statistical Methods and Application, 19(3):333-354.

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.

See Also

rmx, confint

Examples

ind <- rbinom(100, size=1, prob=0.05) 
x <- rnorm(100, mean=ind*3, sd=(1-ind) + ind*9)
res <- rmx(x, eps.lower = 0.01, eps.upper = 0.1)
confint(res)
confint(res, method = "as.bias")
#confint(res, method = "boot", R = 999)

stamats/rmx documentation built on Sept. 29, 2023, 7:13 p.m.