Estimation of level set trees using adaptive partitions |
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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
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Publish Date: | 2017-12-02 |
Description: |
AbstractWe 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
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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 |