Abstract Details
Activity Number:
|
399
|
Type:
|
Invited
|
Date/Time:
|
Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
Abstract - #306988 |
Title:
|
Maximum Likelihood Estimation of a Directed Acyclic Gaussian Graph
|
Author(s):
|
Yiping Yuan and Xiaotong Shen*+ and Wei Pan
|
Companies:
|
University of Minnesota and University of Minnesota and University of Minnesota
|
Keywords:
|
Directed acyclic graph ;
nonconvex ;
gene networks
|
Abstract:
|
Directed acyclic graphs have been widely used to describe causal relations among interacting units. Estimation of a directed acyclic graph presents a great challenge without prior knowledge about the order of interacting units, where the number of enumeration of potential directions grows super-exponentially. A traditional method usually estimates directions locally and sequentially, and hence results in biased estimation. In this paper, we propose a global approach to determine all directions simultaneously, through constrained maximum likelihood with nonconvex constraints reinforcing a directed acyclic graph requirement. Computationally, we propose an efficient algorithm based on a projection-based accelerated gradient method and difference convex programming for approximating nonconvex constrained sets. Numerically, we demonstrate that the method leads to accurate parameter estimation, in parameter estimation as well as identifying graphical structures. Moreover, an application to gene network analysis will be described.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.