Graph Selection with GGMselect
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Christophe Giraud
Applications on inference of biological networks have raised a strong interest in the problem of graph estimation in high-dimensional Gaussian graphical models. To handle this problem, we propose a two-stage procedure which first builds a family of candidate graphs from the data, and then selects one graph among this family according to a dedicated criterion. This estimation procedure is shown to be consistent in a high-dimensional setting, and its risk is controlled by a non-asymptotic oracle-like inequality. The procedure is tested on a real data set concerning gene expression data, and its performances are assessed on the basis of a large numerical study.The procedure is implemented in the R-package GGMselect available on the CRAN.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- Exploring Multicollinearity Using a Random Matrix Theory Approach
- The Beta-Binomial SGoF method for multiple dependent tests
- Detecting Sample Misidentifications in Genetic Association Studies
- Borrowing Information Across Genes and Experiments for Improved Error Variance Estimation in Microarray Data Analysis
- Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies
- The practical effect of batch on genomic prediction
- Normalization, bias correction, and peak calling for ChIP-seq
- Combining Multiple Laser Scans of Spotted Microarrays by Means of a Two-Way ANOVA Model
- Empirical Bayes Interval Estimates that are Conditionally Equal to Unadjusted Confidence Intervals or to Default Prior Credibility Intervals
- Detection of Differentially Expressed Gene Sets in a Partially Paired Microarray Data Set
- Non-Iterative, Regression-Based Estimation of Haplotype Associations with Censored Survival Outcomes
- Graph Selection with GGMselect
- Sample Size Calculations for Designing Clinical Proteomic Profiling Studies Using Mass Spectrometry
- A New Approach for the Joint Analysis of Multiple Chip-Seq Libraries with Application to Histone Modification
- Software Communication
- GENOVA: Gene Overlap Analysis of GWAS Results
