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

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dc.contributor Adams, Benjamin Michael
dc.contributor Barrett, Bruce E.
dc.contributor Keskin, Burcu Baris
dc.contributor Moore, Robert L.
dc.contributor Perry, Marcus
dc.contributor.advisor Chakraborti, Subhabrata McCracken, Amanda Kaye 2017-04-26T14:22:22Z 2017-04-26T14:22:22Z 2012
dc.identifier.other u0015_0000001_0000945
dc.identifier.other McCracken_alatus_0004D_11205
dc.description Electronic Thesis or Dissertation
dc.description.abstract Since 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.
dc.format.extent 153 p.
dc.format.medium electronic
dc.format.mimetype application/pdf
dc.language English
dc.language.iso en_US
dc.publisher University of Alabama Libraries
dc.relation.ispartof The University of Alabama Electronic Theses and Dissertations
dc.relation.ispartof The University of Alabama Libraries Digital Collections
dc.relation.hasversion born digital
dc.rights All rights reserved by the author unless otherwise indicated.
dc.subject.other Statistics
dc.title Contributions to joint monitoring of location and scale parameters: some theory and applications
dc.type thesis
dc.type text University of Alabama. Dept. of Information Systems, Statistics, and Management Science Applied Statistics The University of Alabama doctoral Ph.D.

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