Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume 104, Issue 486, Pages 735-746Publisher
AMER STATISTICAL ASSOC
DOI: 10.1198/jasa.2009.0126
Keywords
Concentration network; Genetic regulatory network; High-dimension-low-sample-size; Lasso; Shooting
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Funding
- Direct For Mathematical & Physical Scien [806128] Funding Source: National Science Foundation
- Division Of Mathematical Sciences [806128] Funding Source: National Science Foundation
- NIGMS NIH HHS [R01 GM082802, R01 GM082802-01A1] Funding Source: Medline
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In this article, we propose a computationally efficient approach-space (Sparse PArtial Correlation Estimation)-for selecting nonzero partial correlations under the high-dimension-low-sample-size setting. This method assumes the overall sparsity of the partial correlation matrix and employs sparse regression techniques for model fitting. We illustrate the performance of space by extensive simulation studies. It is shown that space performs well in both nonzero partial correlation selection and the identification of hub variables, and also outperforms two existing methods. We then apply space to a microarray breast cancer dataset and identify a set of hub genes that may provide important insights on genetic regulatory networks. Finally, we prove that, under a set of suitable assumptions. the proposed procedure is asymptotically consistent in terms of model selection and parameter estimation.
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