Estimation of level set trees using adaptive partitions

Holmström, Lasse; Karttunen, Kyösti; Klemelä, Jussi (2016-12-02)
 
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https://doi.org/10.1007/s00180-016-0702-2

Holmström, Lasse
Karttunen, Kyösti
Klemelä, Jussi
Springer Nature
02.12.2016

Holmström, L., Karttunen, K., Klemelä, J. (2017) Estimation of level set trees using adaptive partitions. Computational Statistics, 32 (3), 1139-1163. doi:10.1007/s00180-016-0702-2

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© Springer-Verlag Berlin Heidelberg 2016. The final publication is available at Springer via http://dx.doi.org/10.1007/s00180-016-0702-2
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1007/s00180-016-0702-2
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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≈8) and for moderate sample sizes (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.

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