Compute a Huber M-estimator of location and scatter, which is reasonably robust for a small number of variables.
covHuber(x, control = covControl(...), ...)a numeric matrix or data frame.
a list of tuning parameters as generated by
covControl.
additional arguments can be used to specify tuning parameters
directly instead of via control.
An object of class "covHuber" with the following components:
a numeric vector containing the location vector estimate.
a numeric matrix containing the scatter matrix estimate.
numeric; probability for the quantile of the \(\chi^{2}\) distribution used as cutoff point in the Huber weight function.
a numeric vector containing the relative robustness weights for the observations.
numeric; correction for Fisher consistency under multivariate normal distributions.
a logical indicating whether the iterative reweighting algorithm converged.
an integer giving the number of iterations required to obtain the solution.
An iterative reweighting algorithm is used to compute the Huber M-estimator. The Huber weight function thereby corresponds to a convex optimization problem, resulting in a unique solution.
Huber, P.J. (1981) Robust statistics. John Wiley & Sons.
Zu, J. and Yuan, K.-H. (2010) Local influence and robust procedures for mediation analysis. Multivariate Behavioral Research, 45(1), 1--44.