Estimation with stable disturbances

dc.contributor.advisorCarvalho, Carlos Marinho, 1978-
dc.creatorGhaffari, Novinen
dc.date.accessioned2015-03-16T17:30:50Zen
dc.date.issued2014-05en
dc.date.submittedMay 2014en
dc.date.updated2015-03-16T17:30:51Zen
dc.descriptiontexten
dc.description.abstractThe family of stable distributions represents an important generalization of the Gaussian family; stable random variables obey a generalized central limit theorem where the assumption of finite variance is replaced with one of power law decay in the tails. Possessing heavy tails, asymmetry, and infinite variance, non-Gaussian stable distributions can be suitable for inference in settings featuring impulsive, possibly skewed noise. A general lack of analytical form for the densities and distributions of stable laws has prompted research into computational methods of estimation. This report introduces stable distributions through a discussion of their basic properties and definitions in chapter 1. Chapter 2 surveys applications, and chapter 3 discusses a number of procedures for inference, with particular attention to time series models in the ARMA setting. Further details and an application can be found in the appendices.en
dc.description.departmentStatisticsen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/29165en
dc.language.isoenen
dc.subjectStable distributionsen
dc.subjectCharacteristic functionen
dc.subjectLévy processen
dc.subjectARMA modelen
dc.titleEstimation with stable disturbancesen
dc.typeThesisen
thesis.degree.departmentStatisticsen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Statisticsen

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