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In lookout: Leave One Out Kernel Density Estimates for Outlier Detection

lookout: Leave One Out Kernel Density Estimates for Outlier Detection

Description

Outlier detection using leave-one-out kernel density estimates and extreme value theory. The bandwidth for kernel density estimates is computed using persistent homology, a technique in topological data analysis. Using peak-over-threshold method, a generalized Pareto distribution is fitted to the log of leave-one-out kde values to identify outliers.

Author(s)

Maintainer: Sevvandi Kandanaarachchi [e-mail] (ORCID)

Authors:

• Rob Hyndman [e-mail] (ORCID)

Other contributors:

• Chris Fraley [e-mail] [contributor]

See Also

Useful links:

• https://sevvandi.github.io/lookout/

lookout documentation built on Oct. 14, 2022, 1:09 a.m.