mer: Minimum Estimated Risk (MER) M-Estimator

In robsurvey: Robust Survey Statistics Estimation

Minimum Estimated Risk (MER) M-Estimator

Description

mer is an adaptive M-estimator of the weighted mean or total. It is defined as the estimator that minimizes the estimated mean square error, mse, of the estimator under consideration.

Usage

mer(object, verbose = TRUE, max_k = 10, init = 1, method = "Brent",
    optim_args = list())

Arguments

object	an object of class svystat_rob.
verbose	[logical] indicating whether additional information is printed to the console (default: TRUE).
init	[numeric] determines the left boundary value of the search interval and the initial value of the search; we must have init < max_k.
method	[character] the method of optim to be used.
max_k	[numeric vector] defines the right boundary value of the search interval (default: max_k = 1000)
optim_args	[list]: arguments passed on to optim.

Details

Package survey must be loaded in order to use the functions.

MER-estimators are available for the methods svymean_huber, svytotal_huber, svymean_tukey and svytotal_tukey.

Value

Object of class svystat_rob

References

Hulliger, B. (1995). Outlier Robust Horvitz-Thompson Estimators. Survey Methodology 21, 79–87.

See Also

Overview (of all implemented functions)

Examples

library(survey)
data(losdata)
dn <- svydesign(ids = ~1, fpc = ~fpc, weights = ~weight, data = losdata)

# M-estimator of the total with tuning constant k = 8
m <- svymean_huber(~los, dn, type = "rhj", k = 8)

# MER estimator
mer(m)

robsurvey documentation built on Jan. 6, 2023, 5:09 p.m.