PARAMETER ESTIMATION FOR THE GENERALIZED GAUSSIAN NOISE MODEL

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dc.contributor.advisorAazhang, Behnaam
dc.creatorVaranasi, Mahesh Kumar
dc.date.accessioned2007-08-20T23:39:41Z
dc.date.available2007-08-20T23:39:41Z
dc.date.issued1987
dc.description.abstractThe primary objective of this study is to propose an estimator of the parameters of the generalized Gaussian noise model with desirable asymptotic properties, namely, asymptotic consistency and asymptotic efficiency. Three estimators are proposed and analyzed. The relative merits and demerits of these estimators are pointed out through an analysis of their asymptotic variances and the computational complexity involved in each estimation procedure. It will be established that while the moment-method is computationally expedient, the maximum likelihood estimator is asymptotically efficient. In addition, the asymptotic relative efficiency of the moment-method with respect to the efficient likelihood estimator is found to be high in the region of the parameter space of practical interest. The maximum likelihood estimation procedure, on the other hand, is found to be computationally cumbersome. The use of the moment-method estimator as a first approximation leads to a computationally expedient and asymptotically consistent and efficient moment/Newton-step estimator.
dc.format.mimetypeapplication/pdf
dc.identifier.callnoTHESIS E.E. 1987 VARANASI
dc.identifier.citationVaranasi, Mahesh Kumar. "PARAMETER ESTIMATION FOR THE GENERALIZED GAUSSIAN NOISE MODEL." (1987) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/17023">https://hdl.handle.net/1911/17023</a>.
dc.identifier.urihttps://hdl.handle.net/1911/17023
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectElectronics
dc.subjectElectrical engineering
dc.titlePARAMETER ESTIMATION FOR THE GENERALIZED GAUSSIAN NOISE MODEL
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
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