Asset Details
Do you wish to reserve the book?
PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs
Do you wish to request the book?
PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs
2016
Overview
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.
Publisher
Blackwell Publishing Ltd,International Biometric Society
Subject
/ Biomarkers, Tumor - genetics
/ biometry
/ Breast Neoplasms - epidemiology
/ Cancer
/ DAG
/ Data Interpretation, Statistical
/ Female
/ Gene Expression Profiling - methods
/ Genetic Predisposition to Disease - epidemiology
/ Genetic Predisposition to Disease - genetics
/ Graphs
/ Humans
/ Neoplasm Proteins - genetics
/ patients
/ Skeleton
