GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models

GMCM: Unsupervised Clustering and Meta-Analysis Using Gaussian Mixture Copula Models

Methods for clustering in unsupervised learning are an important part of the statistical toolbox in numerous scientific disciplines. Tewari, Giering, and Raghunathan (2011) proposed to use so-called Gaussian mixture copula models (GMCM) for general unsupervised learning based on clustering. Li, Brow...

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Bibliographic Details
Main Authors: Anders Ellern Bilgrau, Poul Svante Eriksen, Jakob Gulddahl Rasmussen, Hans Erik Johnsen, Karen Dybkaer, Martin Boegsted
Format: Article
Language:English
Published: Foundation for Open Access Statistics 2016-04-01
Series:Journal of Statistical Software
Subjects:
GMCM
unsupervised learning
clustering
high-dimensional experiments
meta-analysis
reproducibility
evidence aggregation
copulas
p value combination
idr
Rcpp
R
C++
Online Access:https://www.jstatsoft.org/index.php/jss/article/view/2620

Internet

https://www.jstatsoft.org/index.php/jss/article/view/2620

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