The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …

Growth-curve models are generalized multivariate analysis-of-variance models. The basic
idea of the models is to use different polynomials to fit different treatment groups involved in …

In many classification problems objects should be rejected when the confidence in their
classification is too low. An example is a face recognition problem where the faces of a …

In this article we use projection pursuit methods to develop a procedure for detecting outliers
in a multivariate time series. We show that testing for outliers in some projection directions …

Equivariance and invariance issues arise as a fundamental but often problematic aspect of
multivariate statistical analysis. For multivariate quantile and related functions, we provide …

Projection pursuit is a multivariate statistical technique aimed at finding interesting low-
dimensional data projections by maximizing a measure of interestingness commonly known …

A criterion for robust estimation of location and covariance matrix is considered, and its
application in outlier labeling is discussed. This method, unlike the methods based on MVE …

The median absolute deviation from the median (MAD) is an important robust univariate
spread measure. It also plays important roles with multivariate data through statistics based …

This research demonstrates the adverse implications of using non-robust statistical methods
for detecting anomalies in hyperspectral image data, and proposes the use of multivariate …

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 …