Estimation of level set trees using adaptive partitions | Zendy

Lasse Holmström | Zendy; Kyösti Karttunen | Zendy; Jussi Klemelä | Zendy

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Estimation of level set trees using adaptive partitions

Author(s) -

Lasse Holmström,

Kyösti Karttunen,

Jussi Klemelä

Publication year - 2016

Publication title -

computational statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.494

H-Index - 44

eISSN - 1613-9658

pISSN - 0943-4062

DOI - 10.1007/s00180-016-0702-2

Subject(s) - partition (number theory) , mathematics , approx , tree (set theory) , multivariate statistics , computation , statistics , kernel (algebra) , set (abstract data type) , kernel density estimation , sample space , function (biology) , sample (material) , algorithm , computer science , combinatorics , estimator , programming language , operating system , chemistry , chromatography , evolutionary biology , biology

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 ($$d\\approx 8$$dź8) and for moderate sample sizes ($$n\\approx $$nź 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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