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A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics
被引:1051
作者:
Schäfer, J
[1
]
Strimmer, K
[1
]
机构:
[1] Univ Munich, Dept Stat, D-80539 Munich, Germany
来源:
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY
|
2005年
/
4卷
关键词:
shrinkage;
covariance estimation;
small n;
large p" problem;
graphical Gaussian model (GGM);
genetic network;
gene expression;
D O I:
10.2202/1544-6115.1175
中图分类号:
Q5 [生物化学];
Q7 [分子生物学];
学科分类号:
071010 ;
081704 ;
摘要:
Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a novel shrinkage covariance estimator that exploits the Ledoit-Wolf (2003) lemma for analytic calculation of the optimal shrinkage intensity. Subsequently, we apply this improved covariance estimator (which has guaranteed minimum mean squared error, is well-conditioned, and is always positive definite even for small sample sizes) to the problem of inferring large-scale gene association networks. We show that it performs very favorably compared to competing approaches both in simulations as well as in application to real expression data.
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页码:1 / 30
页数:32
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