PCA based on Multivariate MM-estimators with Fast and Robust Bootstrap
We provide R code and MATLAB code to compute robust PCA estimates based on
multivariate MM-estimators of location and shape and to perform inference
based on the fast and robust bootstrap. The code is well documented with
instructions on its use. Questions can be sent to
Gert Willems.
R code
Main function to compute robust PCA based on an MM-estimate of shape with
inference based on the fast and robust bootstrap: MMPCA.R
Necessary additional function that computes the multivariate MM-estimates of
location and shape: multiMM.R
Necessary additional function that computes S-estimates of
location and scatter to find the initial S-scale for the MM-estimator:
fastSmulti_location.R
Necessary additional function that executes the fast and robust
bootstrap for MM-estimates:
multiMMRobBoot_location.R
Necessary additional function that computes empirical influences for MM estimates
as needed to construct BCa bootstrap intervals:
einfsMM.R
MATLAB code
Main function to compute robust PCA based on an MM-estimate of shape with
inference based on the fast and robust bootstrap: MMPCA.m
Necessary additional function that executes the fast and robust
bootstrap for MM-estimates: multiMMRobBoot.m
Necessary additional function that computes the multivariate MM-estimates of
location and shape as well as the initial S-scale obtained from S-estimates of
location and scatter: multiMM.m
Reference
Salibian-Barrera, M., Van Aelst, S., and Willems, G. (2005), "
PCA based on Multivariate MM-estimators with Fast and Robust Bootstrap, "
Journal of the American Statistical Association , 101, 1198 - 1211.
Paper