Partial Correlation Estimation by Joint Sparse Regression Models
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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.
A paper based on this report has been accepted for publication on Journal of the American Statistical Association(http://www.amstat.org/publications/JASA/)
A paper based on this report has been accepted for publication on Journal of the American Statistical Association(http://www.amstat.org/publications/JASA/)