Polar Depth is introduced for heavy-tailed distributions, proven to converge for large-norm observations to the limiting polar depth, with applications to anomaly detection among extremes.
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Defines Wasserstein spatial depth for distributions, proves invariance and robustness properties, establishes consistency and asymptotic normality of a plug-in estimator, and supplies a two-sample test.
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Polar Depth for Potentially Heavy-Tailed Data
Polar Depth is introduced for heavy-tailed distributions, proven to converge for large-norm observations to the limiting polar depth, with applications to anomaly detection among extremes.
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Wasserstein Spatial Depth
Defines Wasserstein spatial depth for distributions, proves invariance and robustness properties, establishes consistency and asymptotic normality of a plug-in estimator, and supplies a two-sample test.