Robust Dispersion Measures in the Presence of Outliers

When data is contaminated by outliers, non-robust measures of dispersion, such as standard deviation, do not reflect the dispersion of the uncontaminated data. Robust measures like trimmed variance or dispersion give a more reliable result.

You can model contaminated data by adding to the original sample a small normal component with high variance. Then analyze the dispersion.

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Visualize the reliability of these measures by performing a Monte Carlo simulation.

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For data without outliers, dispersion is close to the standard deviation.

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