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dc.contributor.authorRibeiro, Vinay Joseph
Riedi, Rudolf H.
Baraniuk, Richard G.
dc.creatorRibeiro, Vinay Joseph
Riedi, Rudolf H.
Baraniuk, Richard G.
dc.date.accessioned 2007-10-31T01:00:52Z
dc.date.available 2007-10-31T01:00:52Z
dc.date.issued 2006-01-15
dc.date.submitted 2006-02-23
dc.identifier.urihttp://hdl.handle.net/1911/20260
dc.description Journal Paper
dc.description.abstract In this paper, we determine which non-random sampling of fixed size gives the best linear predictor of the sum of a finite spatial population. We employ different multiscale superpopulation models and use the minimum mean-squared error as our optimality criterion. In a multiscale superpopulation tree models, the leaves represent the units of the population, interior nodes represent partial sums of the population, and the root node represents the total sum of the population. We prove that the optimal sampling pattern varies dramatically with the correlation structure of the tree nodes. While uniform sampling is optimal for trees with "positive correlation progression," it provides the worst possible sampling with "negative correlation progression." As an analysis tool, we introduce and study a class of independent innovations trees that are of interest in their own right. We derive a fast water-filling algorithm to determine the optimal sampling of the leaves to estimate the root of an independent innovations tree.
dc.description.sponsorship Defense Advanced Research Projects Agency
dc.description.sponsorship National Science Foundation
dc.description.sponsorship National Science Foundation
dc.description.sponsorship National Science Foundation
dc.language.iso eng
dc.subjectmultiscale stochastic processes
finite population
spatial data
networks
sampling
convex
concave
optimization
trees
sensor networks
dc.subject.otherSignal Processing for Networking
dc.title Optimal Sampling Strategies for Multiscale Stochastic Processes
dc.type Journal article
dc.citation.bibtexName article
dc.citation.journalTitle Institute of Mathematical Statistics Lecture Notes - Monograph Series
dc.date.modified 2006-03-08
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordmultiscale stochastic processes
finite population
spatial data
networks
sampling
convex
concave
optimization
trees
sensor networks
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1214/074921706000000509
dc.identifier.citation V. J. Ribeiro, R. H. Riedi and R. G. Baraniuk, "Optimal Sampling Strategies for Multiscale Stochastic Processes," Institute of Mathematical Statistics Lecture Notes - Monograph Series, 2006.


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  • ECE Publications [1212]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.

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