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Evaluating Outlier Identification Tests: Mahalanobis D Squared and Comrey Dk.

J L Rasmussen

    Multivariate Behavioral Research
    |January 15, 2016
    PubMed
    Summary

    Mahalanobis D squared demonstrated superior outlier detection compared to Comrey

    Area of Science:

    • Statistics
    • Data Analysis
    • Outlier Detection

    Background:

    • Outlier detection is crucial in statistical analysis to ensure data integrity.
    • Comrey's Dk statistic was proposed as a sensitive alternative to Mahalanobis D squared for identifying outliers.
    • The potential advantage of Dk lies in its sensitivity to outliers affecting correlation coefficients.

    Purpose of the Study:

    • To compare the performance of Comrey's Dk and Mahalanobis D squared outlier detection statistics.
    • To evaluate hit rates, false alarm rates, overlap, and the impact on correlation coefficients after outlier removal.

    Main Methods:

    • A Monte Carlo simulation was employed for a robust comparison.
    • Key performance metrics included hit rates, false alarm rates, and outlier identification overlap.

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  • The effect of outlier removal on resulting correlation coefficients was analyzed.

    • Main Results:

    • Mahalanobis D squared exhibited a higher hit rate than Dk, with comparable false alarm rates.
    • The two statistics identified the same outliers in 19% to 55% of cases.
    • Outlier removal using Mahalanobis D squared resulted in correlations closer to population values than Dk.

    Conclusions:

    • Mahalanobis D squared is preferable to Dk for outlier detection under the simulated conditions.
    • The study provides empirical evidence supporting the efficacy of Mahalanobis D squared over Dk.