This revised book provides a thorough explanation of the foundation of robust methods,
incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and …

Outliers presence may lead to misdetection on out-of-control observations in Phase II,
therefore, they should be cleaned in Phase I. This paper proposes PCA Mix based T 2 chart …

Detecting outliers for multivariate data is difficult and does not work by visual inspection.
Mahalanobis distance (MD) has been a classical method to detect outliers in multivariate …

The Maximum Likelihood Estimator (MLE) and Extended Quasi-Likelihood (EQL) estimator
have commonly been used to estimate the unknown parameters within the joint modeling of …

Mahalanobis distance (MD) is a classical method to detect outliers for multivariate data.
However, classical mean and covariance matrix in MD suffered from masking and swamping …

In multivariate data, outliers are difficult to detect especially when the dimension of the data
increase. Mahalanobis distance (MD) has been one of the classical methods to detect …

Detecting outliers in high dimension datasets remains a challenging task. Under this
circumstance, robust location and scale estimators are usually proposed in place of the …

Principal Component Analysis (PCA) is a technique to transform the original set of variables
into a smaller set of linear combinations that account for most of the original set variance …

Mahalanobis square distances (MSDs) based on robust estimators improves outlier
detection performance in multivariate data. However, the unbiasedness of robust estimators …

This paper proposes a reliable minimum vector variance regression algorithm for robust
supervised impervious mapping. The mapping is done with a conventional two phase …