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PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs.

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Date
2016-03
Authors
Ha, Min Jin
Sun, Wei
Xie, Jichun
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229
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Abstract
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.
Type
Journal article
Subject
DAG
High dimensional
Log penalty
PC-algorithm
Penalized regression
Skeleton
Biomarkers, Tumor
Breast Neoplasms
Computer Simulation
Data Interpretation, Statistical
Female
Gene Expression Profiling
Genetic Markers
Genetic Predisposition to Disease
Humans
Models, Statistical
Neoplasm Proteins
Prevalence
Reproducibility of Results
Risk Factors
Sensitivity and Specificity
Permalink
https://hdl.handle.net/10161/10825
Published Version (Please cite this version)
10.1111/biom.12415
Publication Info
Ha, Min Jin; Sun, Wei; & Xie, Jichun (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.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Xie

Jichun Xie

Associate Professor of Biostatistics & Bioinformatics
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