ABSTRACT High Leverage Points (HLPs) are outlying observations in the X-directions. It is
very imperative to detect HLPs because the computed values of various estimates are …

Outliers are abnormal data, and the detection of outliers in multivariate data has always
been of interest. Unlike univariate data, outlier detection for multivariate data is insufficient …

Data in practice are often of high dimension and multivariate in nature. Detection of outliers
has been one of the problems in multivariate analysis. Detecting outliers in multivariate data …

In practice, multivariate data frequently include outliers. Since outliers can cause incorrect
conclusions, a robust estimator must be used to analyze the data as the robust estimator is …

Marginal Integration (MI) is a statistical method that is extensively employed to estimate
component functions of the nonparametric additive models. The shortcoming of the purely …

Outliers are abnormal data, and the detection of outliers in multivariate data has always
been of interest. Unlike univariate data, outlier detection for multivariate data is insufficient …

Data clustering is an unsupervised classification method aimed at creating groups of
objects, or clusters that are distinct. Among the clustering techniques, K-means is the most …