Estimation of level set trees using adaptive partitions

Lasse Holmström (), Kyösti Karttunen () and Jussi Klemelä ()
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Lasse Holmström: University of Oulu
Kyösti Karttunen: University of Oulu

Computational Statistics, 2017, vol. 32, issue 3, No 15, 1139-1163

Abstract: 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 ( $$d\approx 8$$ d ≈ 8 ) and for moderate sample sizes ( $$n\approx $$ n ≈ 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.

Keywords: Cluster analysis; Flow cytometry; Kernel density estimation; Mode detection; Recursive partitioning (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00180-016-0702-2

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