Resource title

Robust Estimators are Hard to Compute

Resource image

image for OpenScout resource :: Robust Estimators are Hard to Compute

Resource description

In modern statistics, the robust estimation of parameters of a regression hyperplane is a central problem. Robustness means that the estimation is not or only slightly affected by outliers in the data. In this paper, it is shown that the following robust estimators are hard to compute: LMS, LQS, LTS, LTA, MCD, MVE, Constrained M estimator, Projection Depth (PD) and Stahel-Donoho. In addition, a data set is presented such that the ltsReg-procedure of R has probability less than 0.0001 of finding a correct answer. Furthermore, it is described, how to design new robust estimators.

Resource author

Thorsten Bernholt

Resource publisher

Resource publish date

Resource language


Resource content type


Resource resource URL

Resource license

Adapt according to the presented license agreement and reference the original author.