Outlier Detection and Editing Procedures for Continuous Multivariate Data

Published in: Journal of Official Statistics, v. 22, no. 3, 2006, pp. 487-506

Posted on RAND.org on December 31, 2005

by Bonnie Ghosh-Dastidar, J. L. Schafer

In large datasets, outliers may be difficult to find using informal inspection and graphical displays, particularly when there are missing values. We present a semi-automatic method of outlier detection for continuous, multivariate survey data that is designed to identify outlying cases and suggest potential errors on a case-by-case basis, in the presence of missing data. Our method relies on an explicit probability model for the data. The raw data with outliers is described by a contaminated multivariate normal distribution, and an EM algorithm is applied to obtain robust estimates of the means and covariances in the presence of missing values. Mahalanobis distances are computed to identify potential outliers and offending variables. The procedure is implemented in a software product, which detects outliers and suggests edits to remove offending values. We apply the algorithm to preliminary body-measurement data from the Third National Health and Nutrition Examination Survey, Phase I (1988'1991). This method works quite generally for continuous survey data, and is particularly useful when inter-variable correlations are strong.

This report is part of the RAND Corporation External publication series. Many RAND studies are published in peer-reviewed scholarly journals, as chapters in commercial books, or as documents published by other organizations.

Our mission to help improve policy and decisionmaking through research and analysis is enabled through our core values of quality and objectivity and our unwavering commitment to the highest level of integrity and ethical behavior. To help ensure our research and analysis are rigorous, objective, and nonpartisan, we subject our research publications to a robust and exacting quality-assurance process; avoid both the appearance and reality of financial and other conflicts of interest through staff training, project screening, and a policy of mandatory disclosure; and pursue transparency in our research engagements through our commitment to the open publication of our research findings and recommendations, disclosure of the source of funding of published research, and policies to ensure intellectual independence. For more information, visit www.rand.org/about/principles.

The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.