Algorithms for the manipulation of level sets of
nonparametric density estimates
"Algorithms for the manipulation of level sets of
nonparametric density estimates"
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We present algorithms for finding the level set tree of
a multivariate density estimate.
That is, we find the separated components of level sets of the estimate
for a series of levels, gather information on the separated
components, such as volume and barycenter, and present
the information together with the tree structure of the
The algorithm proceeds by first building a binary tree
which partitions the support of the density estimate,
followed by bottom-up travels of this tree
during which we join those parts of the level sets which touch each other.
As a byproduct we present an algorithm for evaluating
a kernel estimate on a large multidimensional grid.
Since we find the barycenters of the separated components of the
level sets also for high levels,
our method finds the locations of local extremes of the estimate.
We give additional psudocodes
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