Estimation of level set trees using adaptive partitions

被引：3

作者：

Holmstrom, Lasse ^{[1
]}

Karttunen, Kyosti ^{[2
]}

Klemela, Jussi

机构：

[1] Univ Oulu, Dept Math Sci, Oulu, Finland

[2] Univ Oulu, CEMIS Oulu, Oulu, Finland

来源：

COMPUTATIONAL STATISTICS | 2017年 / 32卷 / 03期

关键词：

Cluster analysis; Flow cytometry; Kernel density estimation; Mode detection; Recursive partitioning; FLOW-CYTOMETRY DATA; NONPARAMETRIC-ESTIMATION; DENSITY CONTOUR; VISUALIZATION; RATES;

D O I：

10.1007/s00180-016-0702-2

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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 approximate to 8) and for moderate sample sizes (n approximate to 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.

引用

页码：1139 / 1163

页数：25

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