Info: Compute Absolute and Relative Information

In stamats/rmx: Radius-Minimax Estimators

Compute Absolute and Relative Information

Description

Functions to compute absolute and relative information of asymptotically linear estimators.

Usage

absInfo(object, ...)
relInfo(object, ...)

## S3 method for class 'rmx'
absInfo(object, ...)
## S3 method for class 'rmx'
relInfo(object, ...)

Arguments

object	object of S3 class rmx.
...	further arguments passed through.

Details

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

In case of optimally-robust RMX estimators computed with function rmx (S3 class rmx), the absolute and relative information is computed. For more details about the concept we refer to Kohl (2005).

Value

Matrix with the data and the computed absolute and relative information, respectively.

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, optIF

Examples

ind <- rbinom(20, size=1, prob=0.05) 
x <- rnorm(20, mean=ind*3, sd=(1-ind) + ind*9)
res <- rmx(x, eps.lower = 0.01, eps.upper = 0.1)
absInfo(res)
relInfo(res)
ML <- rmx(x, eps = 0)
absInfo(ML)
relInfo(ML)

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