Multivariate Outlier Detection and Robust Covariance Matrix Estimation
Year: 2001
Type: article
Abstract: In this article, we present a simple multivariate outlier-detection procedure and a robust estimator for the covariance matrix, based on the use of information obtained from projections onto the directions that maximize and minimize the kurtosis coefficient of the projected data. The properties of this estimator (computational cost, bias) are analyzed and compared with those of other robust estimators described in the literature through simulation studies. The performance of the outlier-detection procedure is analyzed by applying it to a set of well-known examples.
Source: Technometrics
Authors: Daniel Peña, Francisco J Prieto
Institution: Universidad Carlos III de Madrid
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2059659103