How can I identify outliers using a Kernel Density Estimation for multivariate data??
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I have this time-series dataset of freshwater chemicals. I would like to perform KDE on the PCA score matrix (using fex. the first 2 scores) and then find a "cutoff density threshold" in order to detect outliers as samples that lay in the space with a certain (low) density. I am using the kde2d function of Dr Z. Botev.
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2018-4-17
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