m_est {robnptests}

M-estimator of location

Description

m_est calculates an M-estimate of location and its variance for different psi functions.

Usage

m_est(
  x,
  psi,
  k = robustbase::.Mpsi.tuning.default(psi),
  tol = 1e-06,
  max.it = 15,
  na.rm = FALSE
)

Arguments

x

a (non-empty) numeric vector of data values.

psi

kernel used for optimization. Must be one of "bisquare", "hampel" and "huber". The default is "huber".

k

tuning parameter(s) for the respective kernel function, defaults to parameters implemented in .Mpsi.tuning.default(psi) in the package robustbase.

tol

tolerance for convergence. The default is 1e-06.

max.it

the maximum number of iterations. The default is 15.

na.rm

a logical value indicating whether NA values in x and y should be stripped before the computation proceeds. The default is na.rm = FALSE.

Details

To compute the M-estimate, the iterative algorithm described in Maronna et al. (2019) is used. The variance is estimated as in Huber (1981).

If max.it contains decimal places, it is truncated to an integer value.

Value

A named list containing the components:

est

estimated mean.

var

estimated variance.

References

Maronna RA, Martin DR, Yohai VJ, Salibián-Barrera M (2019). Robust Statistics: Theory and Methods (with R), Wiley Series in Probability and Statistics, Second edition edition. Wiley. doi:10.1002/9781119214656.

Huber PJ (1981). Robust Statistics. Wiley, New York. doi:10.1002/0471725250.

Examples

# Generate random sample
set.seed(108)
x <- rnorm(10)

# Computer Huber's M-estimate
m_est(x, psi = "huber")