Partial Correlation Estimation by Joint Sparse Regression Models.

  title={Partial Correlation Estimation by Joint Sparse Regression Models.},
  author={Jie Peng and Pei Wang and Nengfeng Zhou and Ji Zhu},
  journal={Journal of the American Statistical Association},
  volume={104 486},
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… CONTINUE READING
Highly Influential
This paper has highly influenced a number of papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 509 citations. REVIEW CITATIONS

9 Figures & Tables



Citations per Year

510 Citations

Semantic Scholar estimates that this publication has 510 citations based on the available data.

See our FAQ for additional information.