By Topic

Tree-structured statistical modeling via convex optimization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Saunderson, J. ; Dept. of Electr. Eng. & Comput. Sci., Massachusetts Inst. of Technol., Cambridge, MA, USA ; Chandrasekaran, V. ; Parrilo, P.A. ; Willsky, A.S.

We develop a semidefinite-programming-based approach to stochastic modeling with multiscale autoregressive (MAR) processes - a class of stochastic processes indexed by the vertices of a tree. Given a tree and the covariance matrix of the variables corresponding to the leaves of the tree, our procedure aims to construct an MAR process with small state dimensions at each vertex that approximately realizes the given covariance at the leaves. Our method does not require prior specification of the state dimensions at each vertex. Furthermore, we establish a large class of MAR processes for which, given only the index tree and the leaf covariance of the process, our method can recover a parametrization that matches the leaf-covariance and has the correct state dimensions. Finally we demonstrate, using synthetic examples, that given i.i.d. samples of the leaf variables our method can recover the correct state dimensions of an underlying MAR process.

Published in:

Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on

Date of Conference:

12-15 Dec. 2011