The minimum covariance determinant (MCD) method is a highly robust estimator of
multivariate location and scatter, for which a fast algorithm is available. Since estimating the …

Orthogonal regression is one of the prominent approaches for linear regression used to
adjust the estimate of predictor errors. It can be considered as a least square regression with …

Most algorithms for highly robust estimators of multivariate location and scatter start by
drawing a large number of random subsets. For instance, the FASTMCD algorithm of …

Principal component analysis (PCA) is a powerful fault detection and isolation method.
However, the classical PCA which is based on the estimation of the sample mean and …

Total least squares (TLS) can solve the issue of parameter estimation in the errors-
invariables (EIV) model, however, the estimated parameters are affected or even severely …

The theory of Baarda's data snooping—normal and F tests respectively based on the known
and unknown posteriori variance—is applied to detect blunders in errors-invariables (EIV) …

In this contribution, the iteratively reweighted total least squares (IRTLS) method is
introduced as a robust estimation in errors-in-variables (EIV) models. The method is a follow …

We define the minimum covariance determinant functionals for multivariate location and
scatter through trimming functions and establish their existence at any multivariate …

In Cator and Lopuhaä (arXiv: math. ST/0907.0079)[3], an asymptotic expansion for the
minimum covariance determinant (MCD) estimators is established in a very general …

The minimum covariance determinant (MCD) estimator is ubiquitous in multivariate analysis,
the critical step of which is to select a subset of a given size with the lowest sample …