Density Kernel Depth for Outlier Detection in Functional Data

The Density Kernel Depth (DKD) method provides a nuanced approach to detect outliers in functional data, offering clarity in complex datasets and ensuring data quality. Its application becomes crucial as we navigate increasingly intricate data landscapes.

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