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

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dc.contributor Conerly, Michael D.
dc.contributor Chakraborti, Subhabrata
dc.contributor Adams, Benjamin Michael
dc.contributor Melouk, Sharif H.
dc.contributor Mobbs, Houston Shawn
dc.contributor.advisor Perry, Marcus B.
dc.contributor.author Mercado Velasco, Gary Ricardo
dc.date.accessioned 2017-04-26T14:22:25Z
dc.date.available 2017-04-26T14:22:25Z
dc.date.issued 2012
dc.identifier.other u0015_0000001_0001096
dc.identifier.other MercadoVelasco_alatus_0004D_11251
dc.identifier.uri http://ir.ua.edu/handle/123456789/2933
dc.description Electronic Thesis or Dissertation
dc.description.abstract Parametric 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.
dc.format.extent 118 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 On the detection and estimation of changes in a process mean based on kernel estimators
dc.type thesis
dc.type text
etdms.degree.department University of Alabama. Dept. of Information Systems, Statistics, and Management Science
etdms.degree.discipline Applied Statistics
etdms.degree.grantor The University of Alabama
etdms.degree.level doctoral
etdms.degree.name Ph.D.


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