PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs.
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Estimation of the skeleton of a directed acyclic graph (DAG) is of great importance for understanding the underlying DAG and causal effects can be assessed from the skeleton when the DAG is not identifiable. We propose a novel method named PenPC to estimate the skeleton of a high-dimensional DAG by a two-step approach. We first estimate the nonzero entries of a concentration matrix using penalized regression, and then fix the difference between the concentration matrix and the skeleton by evaluating a set of conditional independence hypotheses. For high-dimensional problems where the number of vertices p is in polynomial or exponential scale of sample size n, we study the asymptotic property of PenPC on two types of graphs: traditional random graphs where all the vertices have the same expected number of neighbors, and scale-free graphs where a few vertices may have a large number of neighbors. As illustrated by extensive simulations and applications on gene expression data of cancer patients, PenPC has higher sensitivity and specificity than the state-of-the-art method, the PC-stable algorithm.
Data Interpretation, Statistical
Gene Expression Profiling
Genetic Predisposition to Disease
Reproducibility of Results
Sensitivity and Specificity
Published Version (Please cite this version)10.1111/biom.12415
Publication InfoHa, MJ; Sun, W; & Xie, J (2016). PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs. Biometrics, 72(1). pp. 146-155. 10.1111/biom.12415. Retrieved from https://hdl.handle.net/10161/10825.
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