Low Rank Estimation of Higher Order Statistics

dc.citation.bibtexNamearticleen_US
dc.citation.journalTitleIEEE Transactions on Signal Processingen_US
dc.contributor.authorNowak, Robert Daviden_US
dc.contributor.authorVan Veen, Barry D.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:56:01Z
dc.date.available2007-10-31T00:56:01Z
dc.date.issued1995-12-01en
dc.date.modified2004-11-04en_US
dc.date.submitted2004-01-13en_US
dc.descriptionJournal Paperen_US
dc.description.abstractLow rank estimators for higher order statistics are considered in this paper. Rank reduction methods offer a general principle for trading estimator bias for reduced estimator variance. The bias-variance tradeoff is analyzed for low rank estimators of higher order statistics using a tensor product formulation for the moments and cumulants. In general the low rank estimators have a larger bias and smaller variance than the corresponding full rank estimator. Often a tremendous reduction in variance is obtained in exchange for a slight increase in bias. This makes the low rank estimators extremely useful for signal processing algorithms based on sample estimates of the higher order statistics. The low rank estimators also offer considerable reductions in the computational complexity of such algorithms. The design of subspaces to optimize the tradeoffs between bias, variance, and computation is discussed and a noisy input, noisy output system identification problem is used to illustrate the results.en_US
dc.description.sponsorshipArmy Research Officeen_US
dc.description.sponsorshipNational Science Foundationen_US
dc.identifier.citationR. D. Nowak and B. D. Van Veen, "Low Rank Estimation of Higher Order Statistics," <i>IEEE Transactions on Signal Processing,</i> 1995.
dc.identifier.urihttps://hdl.handle.net/1911/20153
dc.language.isoeng
dc.subjectTemporary*
dc.subject.keywordTemporaryen_US
dc.subject.otherWavelet based Signal/Image Processingen_US
dc.titleLow Rank Estimation of Higher Order Statisticsen_US
dc.typeJournal article
dc.type.dcmiText
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