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Joint estimation of multiple dependent Gaussian graphical models with applications to mouse genomics
- Source :
- Biometrika
- Publication Year :
- 2017
- Publisher :
- Oxford University Press, 2017.
-
Abstract
- SUMMARY Gaussian graphical models are widely used to represent conditional dependencies among random variables. In this paper, we propose a novel estimator for data arising from a group of Gaussian graphical models that are themselves dependent. A motivating example is that of modelling gene expression collected on multiple tissues from the same individual: here the multivariate outcome is affected by dependencies acting not only at the level of the specific tissues, but also at the level of the whole body; existing methods that assume independence among graphs are not applicable in this case. To estimate multiple dependent graphs, we decompose the problem into two graphical layers: the systemic layer, which affects all outcomes and thereby induces cross-graph dependence, and the category-specific layer, which represents graph-specific variation. We propose a graphical EM technique that estimates both layers jointly, establish estimation consistency and selection sparsistency of the proposed estimator, and confirm by simulation that the EM method is superior to a simpler one-step method. We apply our technique to mouse genomics data and obtain biologically plausible results.
- Subjects :
- 0301 basic medicine
Statistics and Probability
FOS: Computer and information sciences
Variable selection
General Mathematics
Gaussian
Feature selection
Machine Learning (stat.ML)
01 natural sciences
010104 statistics & probability
03 medical and health sciences
symbols.namesake
Mouse genomics
Statistics - Machine Learning
Expectation–maximization algorithm
Graphical model
0101 mathematics
EM algorithm
Shrinkage
Independence (probability theory)
Mathematics
Conditional dependence
Applied Mathematics
Estimator
Articles
Agricultural and Biological Sciences (miscellaneous)
030104 developmental biology
Gaussian graphical model
symbols
Statistics, Probability and Uncertainty
General Agricultural and Biological Sciences
Algorithm
Random variable
Sparsity
Subjects
Details
- Language :
- English
- ISSN :
- 14643510 and 00063444
- Volume :
- 103
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- Biometrika
- Accession number :
- edsair.doi.dedup.....e93dab65548c8f5f7302fd938da3d4bc