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


Autoria(s): Ha, MJ; Sun, W; Xie, J
Cobertura

United States

Data(s)

01/03/2016

Resumo

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.

Formato

146 - 155

Identificador

http://www.ncbi.nlm.nih.gov/pubmed/26406114

Biometrics, 2016, 72 (1), pp. 146 - 155

http://hdl.handle.net/10161/10825

1541-0420

Idioma(s)

ENG

Relação

Biometrics

10.1111/biom.12415

Palavras-Chave #DAG #High dimensional #Log penalty #PC-algorithm #Penalized regression #Skeleton
Tipo

Journal Article