On the detection and estimation of changes in a process mean based on kernel estimators

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dc.contributorConerly, Michael D.
dc.contributorChakraborti, Subhabrata
dc.contributorAdams, Benjamin Michael
dc.contributorMelouk, Sharif H.
dc.contributorMobbs, Houston Shawn
dc.contributor.advisorPerry, Marcus B.
dc.contributor.authorMercado Velasco, Gary Ricardo
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2017-04-26T14:22:25Z
dc.date.available2017-04-26T14:22:25Z
dc.date.issued2012
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractParametric control charts are very attractive and have been used in the industry for a very long time. However, in many applications the underlying process distribution is not known sufficiently to assume a specific distribution function. When the distributional assumptions underlying a parametric control chart are violated, the performance of the control chart could be potentially affected. Since robustness to departures from normality is a desirable property for control charts, this dissertation reports three separate papers on the development and evaluation of robust Shewhart-type control charts for both the univariate and multivariate cases. In addition, a statistical procedure is developed for detecting step changes in the mean of the underlying process given that Shewhart-type control charts are not very sensitive to smaller changes in the process mean. The estimator is intended to be applied following a control chart signal to aid in diagnosing root cause of change. Results indicate that methodologies proposed throughout this dissertation research provide robust in-control average run length, better detection performance than that offered by the traditional Shewhart control chart and/or the Hotelling's control chart, and meaningful change point diagnostic statistics to aid in the search for the special cause.en_US
dc.format.extent118 p.
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otheru0015_0000001_0001096
dc.identifier.otherMercadoVelasco_alatus_0004D_11251
dc.identifier.urihttp://ir.ua.edu/handle/123456789/2933
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.titleOn the detection and estimation of changes in a process mean based on kernel estimatorsen_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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