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Model robust profile monitoring for the generalized linear mixed model for Phase I analysis

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dc.contributor.authorBandara, Keerthien
dc.contributor.authorAbdel-Salam, Abdel-Salam Gomaaen
dc.contributor.authorBirch, Jeffrey B.en
dc.contributor.departmentStatisticsen
dc.date.accessioned2020-12-03T17:54:51Zen
dc.date.available2020-12-03T17:54:51Zen
dc.date.issued2020-11-02en
dc.description.abstractThe generalized linear mixed model (GLMM) becomes very popular in profile monitoring, especially when the production processes follow nonnormal distribution. In most of the real-life applications in industry, medicine, biology horizontal ellipsis and so on researchers assume that the response variable follows a Bernoulli or Binomial distribution. The majority of previous studies in profile monitoring focused on parametric modeling using the logistic regression model, with both fixed or random effects, under the assumption of correct model specification. This research considers those cases where the parametric logistic regression model for the family of profiles is unknown or at least uncertain. Consequently, we propose two mixed model methods to monitor profiles from the exponential family: a nonparametric (NP) regression method based on the penalized spline regression technique and a semiparametric method (model robust profile monitoring for the generalized linear mixed model) which combines the advantages of both the parametric and NP methods. Several Hotelling T2 charts that have been studied for a binary response variable with replicates for Phase I profile monitoring. The performance of the proposed method is evaluated by using mean squares of errors and probability of signals criteria. The results showed satisfactory performance of the proposed control charts.en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1002/asmb.2587en
dc.identifier.eissn1526-4025en
dc.identifier.issn1524-1904en
dc.identifier.urihttp://hdl.handle.net/10919/101004en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectGLMMen
dc.subjectlogistic regressionen
dc.subjectnonparametricen
dc.subjectprofile monitoringen
dc.subjectsemiparametricen
dc.titleModel robust profile monitoring for the generalized linear mixed model for Phase I analysisen
dc.title.serialApplied Stochastic Models in Business And Industryen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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