Evidence map›Paper›PMID 26594301›Full record

ArticleStatistics and its interface

Energy bagging tree.

Taoyun Cao, Xueqin Wang, Heping Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistics and its interface. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Taoyun CaoSouthern China Research Center of Statistical Science, School of Mathematics and Computational Science,Sun Yat-Sen University,Guangzhou, 510275, China.
Xueqin WangSouthern China Research Center of Statistical Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, 510275, China; Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
Heping ZhangSouthern China Research Center of Statistical Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, 510275, China; Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06520-8034, USA.

Funding

Statistical Methods in Genetic Studies of Substance UseR01DA016750 · NIDA · YALE UNIVERSITY · PI ZHANG, HEPING · 2004 to 2016
$3.3M
NIDA NIH HHS R01 DA016750
6 · The paper itself

Abstract

This paper introduces Energy Bagging Tree (EBT) for multivariate nonparametric regression problems. The EBT makes use of a measure of dispersion constructed from a generalized Gini's mean difference as node impurity, and the tree split function therefore corresponds to the product of energy distance and descendants' proportions. As a non-parametric extension of the between-sample variation in the analysis of variance, this measure of dispersion serves well for EBT in understanding certain complex data. Extensive simulation studies indicate that EBT is highly competitive with existing regression tree methods. We also assess the performance of the EBT through a real data analysis on forest fires.

Indexed as

Energy bagging treeEnergy distanceGeneralized Gini's mean differenceMultivariate nonparametric regression

Identifiers

PMID26594301
PMCPMC4648255

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.