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Outlier Detection and Editing Procedures for Continuous Multivariate Data

Author

Listed:
  • Bonnie Ghosh-Dastider

    (RAND and Princeton University)

  • J. L. Schafer

    (Pennsylvania State University)

Abstract

We present a semi-automatic method of outlier detection for continuous, multivariate survey data. In large datasets, outliers may be difficult to find using informal inspection and graphical displays, particularly when there are missing values. 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. Mahalanobis distances are computed to identify potential outliers. The procedure is implemented in a software product which detects outliers and suggests edits to remove offending values. We apply the algorithm to body-measurement data from the Third National Health and Nutrition Examination Survey. This method works quite generally for continuous survey data, and is particularly useful when inter-variable correlations are strong.

Suggested Citation

  • Bonnie Ghosh-Dastider & J. L. Schafer, 2003. "Outlier Detection and Editing Procedures for Continuous Multivariate Data," Working Papers 297, Princeton University, Woodrow Wilson School of Public and International Affairs, Office of Population Research..
    • Handle: RePEc:pri:opopre:opr0307.pdf
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    More about this item

    Keywords

    Contaminated normal; EM algorithm; outliers; posterior probability;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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