ImpS: Imputed S-estimator

In GSE: Robust Estimation in the Presence of Cellwise and Casewise Contamination and Missing Data

Imputed S-estimator

Description

Computes the simple three-step estimator as described in the rejoinder of Agostinelli et al. (2015).

Usage

ImpS(x, alpha=0.95, method=c("bisquare","rocke"), init=c("emve","emve_c"), ...)

Arguments

x	a matrix or data frame.
alpha	quantile of the reference distribution in the univariate filter step (see gy.filt). Default is 0.95.
method	which loss function to use: 'bisquare', 'rocke'.
init	type of initial estimator. Currently this can either be "emve" (EMVE with uniform sampling, see Danilov et al., 2012) or "emve_c" (EMVE_C with cluster sampling, see Leung and Zamar, 2016). Default is "emve".
...	optional, additional arguments to be passed to GSE.

Details

This function computes the simple three-step estimator as described in the rejoinder in Agostinelli et al. (2015). The procedure has three steps:

In Step I, the method flags and removes cell-wise outliers using the Gervini-Yohai univariate only filter (see gy.filt).

In Step II, the method imputes the filtered cells using coordinate-wise medians.

In Step III, the method applies MVE-S to the filtered and imputed data from Step II (see GSE).

Value

The following gives the major slots in the output S4 object:

mu	Estimated location. Can be accessed via getLocation.
S	Estimated scatter matrix. Can be accessed via getScatter.
xf	Filtered data matrix from the first step of 2SGS. Can be accessed via getFiltDat.

Author(s)

Andy Leung [e-mail], Claudio Agostinelli, Ruben H. Zamar, Victor J. Yohai

References

Agostinelli, C., Leung, A. , Yohai, V.J., and Zamar, R.H. (2015) Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination. TEST.

See Also

GSE, gy.filt

GSE documentation built on Dec. 28, 2022, 1:31 a.m.