Show simple item record

dc.contributor.authorRibeiro, Vinay Joseph
Riedi, Rudolf H.
Baraniuk, Richard G.
dc.creatorRibeiro, Vinay Joseph
Riedi, Rudolf H.
Baraniuk, Richard G.
dc.date.accessioned 2007-10-31T01:00:35Z
dc.date.available 2007-10-31T01:00:35Z
dc.date.issued 2004-12-01
dc.date.submitted 2004-12-13
dc.identifier.citation V. J. Ribeiro, R. H. Riedi and R. G. Baraniuk, "Optimal Sampling Strategies for Multiscale Stochastic Processes," Erich L. Lehmann Symposium, 2004.
dc.identifier.urihttps://hdl.handle.net/1911/20254
dc.description Journal Paper
dc.description.abstract This paper studies multiscale stochastic processes which are random processes organized on the nodes of a tree. The random variables at different levels on the tree represent time series of samples of a stochastic process at different temporal or spatial cales. We focus on classes of multiscale processes with additional statistical structure connecting scales and seek an optimal linear estimator of coarse scale nodes using an incomplete set of nodes at a finer time scale. We prove that the optimal solution for any tree with so-called independent innovations is readily given by a polynomial-time algorithm which we term the water-filling algorithm. The optimal solutions vary dramatically with the correlation structure of the multiscale process. For so-called scale-invariant trees and processes with positive correlation progression through scales, uniformly spaced leaves are optimal and clustered leaves are the worst possible. For processes with negative correlation progression, uniformly spaced leaves are the worst possible. Our results have implications for network traffic estimation, sensor network design, and environmental monitoring.
dc.description.sponsorship Texas Instruments
dc.description.sponsorship Defense Advanced Research Projects Agency
dc.description.sponsorship National Science Foundation
dc.language.iso eng
dc.relation.urihttp://www.jstor.org/stable/4356403
dc.subjectmultiscale stochastic processes
networks
long-range-dependence
sampling
optimal
trees
sensor networks
dc.subject.otherMultiscale Methods
dc.title Optimal Sampling Strategies for Multiscale Stochastic Processes
dc.type Journal article
dc.citation.bibtexName article
dc.citation.journalTitle Erich L. Lehmann Symposium
dc.date.modified 2006-07-05
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordmultiscale stochastic processes
networks
long-range-dependence
sampling
optimal
trees
sensor networks
dc.relation.projecthttp://www.dsp.rice.edu
dc.relation.softwarehttp://www.dsp.rice.edu
dc.type.dcmi Text
dc.type.dcmi Text


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1321]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

Show simple item record