Sentence examples for minimum covariance line from inspiring English sources

Regions near the minimum covariance line are clearly associated with fragmented geographies, like archipelagos or mountain ranges, or the tips of continents.

The Minimum Covariance Determinant (MCD) estimator is a highly robust procedure for estimating the centre and shape of a high dimensional data set.

The reliability and robustness of the normal estimation subroutine was compared against well-known normal estimation methods including the Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) estimators, along with Maximum Likelihood Sample Consensus (MLESAC).

The key novel element of this work is the introduction of robust multivariate statistical methods into the structural health monitoring (SHM) field through use of the minimum covariance determinant estimator (MCD) and the minimum volume enclosing ellipsoid (MVEE).

Moreover, robust minimum covariance determinant estimator (MCDE) is given in Sect.

Alameddine et al. [1] demonstrated a case study to analyze the effectiveness of the minimum covariance determinant MCD, the minimum volume ellipsoid MVE, and M-estimator.

They proposed a robust sparse common spatial pattern (RSCSP) algorithm for optimal EEG channel selection across different sessions, where the estimates of the covariance matrices of EEG measurements are replaced with the robust minimum covariance determinant (MCD) estimates.

One of the most efficient and useful robust multivariate regressions is the minimum covariance determinant, which was proposed by Rousseeuw et al. (2004).

We utilize the Minimum Covariance Determinant (MCD) estimator [16] which yields a robust location estimate for each window that is not highly influenced by outlying locations caused by the limitations of Argos.

The standard Mahalanobis distance can be robustified by substituting for the sample covariance a covariance estimator based on the minimum covariance determinant, minimum volume ellipsoid or a specific S-estimator.

We address this issue by employing the robust minimum covariance determinant to estimate mean and scatter [ 30, 33].