University of Oulu

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

Estimation of level set trees using adaptive partitions

Saved in:
Author: Holmström, Lasse1; Karttunen, Kyösti2; Klemelä, Jussi3
Organizations: 1Department of Mathematical Sciences, University of Oulu, Oulu, Finland
2CEMIS Oulu, University of Oulu, Oulu, Finland
3Helsinki, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe201708108070
Language: English
Published: Springer Nature, 2017
Publish Date: 2017-12-02
Description:

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.

see all

Series: Computational statistics
ISSN: 0943-4062
ISSN-E: 1613-9658
ISSN-L: 0943-4062
Volume: 32
Issue: 3
Pages: 1139 - 1163
DOI: 10.1007/s00180-016-0702-2
OADOI: https://oadoi.org/10.1007/s00180-016-0702-2
Type of Publication: A1 Journal article – refereed
Field of Science: 112 Statistics and probability
Subjects:
Funding: The authors gratefully acknowledge the TEKES funding under project 24301335.
Dataset Reference: The online version of this article (doi:10.1007/s00180-016-0702-2) contains supplementary material, which is available to authorized users.
Copyright information: © Springer-Verlag Berlin Heidelberg 2016. The final publication is available at Springer via http://dx.doi.org/10.1007/s00180-016-0702-2