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 …

The process of detection of outliers is an interesting and important aspect in the analysis of
data, as it could impact the inference. Literature is abundant with procedures for detection …

An outlier is an observation that is inconsistent and deviate markedly from the majority of the
data. The detection of outliers is a crucial part in data analysis since the contaminated …

The objective of this research is detection of outliers in multivariate data employing various
distance measure, particularly using robust regression diagnosis technique. Several …

Methodologies for identifying multivariate outliers are extremely important in statistical
analysis. Outliers may reveal relevant information to variables under investigation. Statistical …

It is well recognized that even a high quality data set tends to contain a remarkable percent
of unusual observations. There are various techniques to detect multivariate outliers. But …

Methodologies for identifying multivariate outliers are extremely important in statistical
analysis. Outliers may reveal relevant information to variables under investigation. Statistical …

There exist multiple methods to detect outliers in multivariate data in the literature, but most
of them require to estimate the covariance matrix. The higher the dimension, the more …

ABSTRACT A number of methods are available to detect outliers in univariate data sets.
Most of these tests are designed to handle one outlier at a time. As soon as an outlier is …

This paper compares two approaches in identifying outliers in multivariate datasets;
Mahalanobis distance (MD) and robust distance (RD). MD has been known suffering from …