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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 |