Journal Article

Partial Correlation Estimation by Joint Sparse Regression Models

Peng, Jie, Zhu, Ji, Wang, Pei, Zhou, Nengfeng

2009

Overview

This article features online supplementary material. 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.

• Publisher: Taylor & Francis,American Statistical Association,Taylor & Francis Ltd
• Subject: Applications / Breast cancer / Cancer / Concentration network / Consistent estimators / Correlation / Correlation analysis / Correlations / Covariance / Covariance matrices / Datasets / Dew / Estimation / Estimators / Exact sciences and technology / General topics / Genes / Genetic regulatory network / Genetics / High-dimension-low-sample-size / Insurance, economics, finance / Lasso / Linear inference, regression / Mathematics / Multivariate analysis / Networks / Parameter estimation / Probability and statistics / Regression analysis / Sample size / Sampling / Sciences and techniques of general use / Shooting / Simulation / Statistical methods / Statistics / Theory and Methods