Partial Correlation Estimation by Joint Sparse Regression Models

• 成果类型: Article
• 署名作者: Peng, Jie; Wang, Pei; Zhou, Nengfeng; Zhu, Ji
• 署名单位: University of California System; University of California Davis; Fred Hutchinson Cancer Center; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.0126
• 发表日期: 2009
• 页码: 735-746
• 关键词: nonconcave penalized likelihood; large covariance matrices; variable selection; networks; gene; graphs

摘要

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.