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Estimation of Contamination Parameters and Identification of Outliers in Multivariate Data

Author

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  • MAIA BERKANE

    (University of California, Los Angeles)

  • P. M. BENTLER

    (University of California, Los Angeles)

Abstract

Multivariate outliers may be modeled using the contaminated multivariate normal distribution with two parameters indicating the percentage of outliers and the degree of contamination. Recent developments in elliptical distribution theory are used to determine estimators of these parameters. These estimators can be used with an index of Mahalanobis distance to identify the multivariate outliers, which can then be eliminated to obtain approximately normal data. The performance of the proposed estimators and outliers rejection procedures are evaluated in a small simulation study.

Suggested Citation

  • Maia Berkane & P. M. Bentler, 1988. "Estimation of Contamination Parameters and Identification of Outliers in Multivariate Data," Sociological Methods & Research, , vol. 17(1), pages 55-64, August.
    • Handle: RePEc:sae:somere:v:17:y:1988:i:1:p:55-64
    DOI: 10.1177/0049124188017001003
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    References listed on IDEAS

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    1. Berkane, Maia & Bentler, P. M., 1986. "Moments of elliptically distributed random variates," Statistics & Probability Letters, Elsevier, vol. 4(6), pages 333-335, October.
    2. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
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    Cited by:

    1. Antonio Punzo & Paul. D. McNicholas, 2017. "Robust Clustering in Regression Analysis via the Contaminated Gaussian Cluster-Weighted Model," Journal of Classification, Springer;The Classification Society, vol. 34(2), pages 249-293, July.
    2. Maruotti, Antonello & Punzo, Antonio, 2017. "Model-based time-varying clustering of multivariate longitudinal data with covariates and outliers," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 475-496.
    3. Morris, Katherine & Punzo, Antonio & McNicholas, Paul D. & Browne, Ryan P., 2019. "Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 145-166.
    4. Rajendran Sriramachandramurthy & Siva K. Balasubramanian & Monica Alexandra Hodis, 2009. "Spyware and Adware: How Do Internet Users Defend Themselves?," American Journal of Business, Emerald Group Publishing Limited, vol. 24(2), pages 41-52, October.

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