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Estimation of level set trees using adaptive partitions
Authors:Lasse Holmström  Kyösti Karttunen  Jussi Klemelä
Institution:1.Department of Mathematical Sciences,University of Oulu,Oulu,Finland;2.CEMIS Oulu,University of Oulu,Oulu,Finland;3.Helsinki,Finland
Abstract:We present methods for the estimation of level sets, a level set tree, and a volume function of a multivariate density function. The methods are such that the computation is feasible and estimation is statistically efficient in moderate dimensional cases (\(d\approx 8\)) and for moderate sample sizes (\(n\approx \) 50,000). We apply kernel estimation together with an adaptive partition of the sample space. We illustrate how level set trees can be applied in cluster analysis and in flow cytometry.
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