Contributions to joint monitoring of location and scale parameters: some theory and applications

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dc.contributorAdams, Benjamin Michael
dc.contributorBarrett, Bruce E.
dc.contributorKeskin, Burcu Baris
dc.contributorMoore, Robert L.
dc.contributorPerry, Marcus
dc.contributor.advisorChakraborti, Subhabrata
dc.contributor.authorMcCracken, Amanda Kaye
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2017-04-26T14:22:22Z
dc.date.available2017-04-26T14:22:22Z
dc.date.issued2012
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractSince their invention in the 1920s, control charts have been popular tools for use in monitoring processes in fields as varied as manufacturing and healthcare. Most of these charts are designed to monitor a single process parameter, but recently, a number of charts and schemes for jointly monitoring the location and scale of processes which follow two-parameter distributions have been developed. These joint monitoring charts are particularly relevant for processes in which special causes may result in a simultaneous shift in the location parameter and the scale parameter. Among the available schemes for jointly monitoring location and scale parameters, the vast majority are designed for normally distributed processes for which the in-control mean and variance are known rather than estimated from data. When the process data are non-normally distributed or the process parameters are unknown, alternative control charts are needed. This dissertation presents and compares several control schemes for jointly monitoring data from Laplace and shifted exponential distributions with known parameters as well as a pair of charts for monitoring data from normal distributions with unknown mean and variance. The normal theory charts are adaptations of two existing procedures for the known parameter case, Razmy's (2005) Distance chart and Chen and Cheng's (1998) Max chart, while the Laplace and shifted exponential charts are designed using an appropriate statistic for each parameter, such as the maximum likelihood estimators.en_US
dc.format.extent153 p.
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otheru0015_0000001_0000945
dc.identifier.otherMcCracken_alatus_0004D_11205
dc.identifier.urihttp://ir.ua.edu/handle/123456789/2924
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.subjectStatistics
dc.titleContributions to joint monitoring of location and scale parameters: some theory and applicationsen_US
dc.typethesis
dc.typetext
etdms.degree.departmentUniversity of Alabama. Department of Information Systems, Statistics, and Management Science
etdms.degree.disciplineApplied Statistics
etdms.degree.grantorThe University of Alabama
etdms.degree.leveldoctoral
etdms.degree.namePh.D.

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