The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Partial Correlation Estimation by Joint Sparse Regression Models
Open accessAbstract
In this paper, we propose a computationally efficient approach -space(Sparse PArtial Correlation Estimation)- for selecting non-zero 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 non-zero partial correlation selection and the identification of hub variables, and also outperforms two existing methods. We then apply space to a microarray breast cancer data set and identify a set of hub genes which 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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Self-citationVariation and genetic control of protein abundance in humans
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Self-citationComparing Statistical Methods for Constructing Large Scale Gene Networks
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Metadata
- Authors
- Peng, J., Wang, P., Zhou, N., & Zhu, J.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2009-06
- Volume
- 104
- Issue
- 486
- Pages
- 735-746
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2001334414
- Cited by
- 688
- References
- 41
- FWCI
- 24.05
- Type
- journal article
- Language
- en
- Open access
- green
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