Jie Peng ¹, Pei Wang ², Nengfeng Zhou ³, Ji Zhu ³

 ¹ Department of Statistics, University of California, Davis, CA 95616.

² Division of Public Health Science, Fred Hutchinson Cancer Research Center,

³ Department of Statistics, University of Michigan, Ann Arbor, MI 48109.

Abstract:

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.

Keywords: concentration network, high-dimension-low-sample-size, lasso, shooting, genetic regulatory network