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Outlier Robust Nonlinear Mixed Model Estimation

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dc.contributor.authorWilliams, James D.en
dc.contributor.authorBirch, Jeffrey B.en
dc.contributor.authorAbdel-Salam, Abdel-Salam Gomaaen
dc.contributor.departmentStatisticsen
dc.date.accessioned2019-05-08T19:46:20Zen
dc.date.available2019-05-08T19:46:20Zen
dc.date.issued2014en
dc.description.abstractIn standard analyses of data well-modeled by a nonlinear mixed model (NLMM), an aberrant observation, either within a cluster, or an entire cluster itself, can greatly distort parameter estimates and subsequent standard errors. Consequently, inferences about the parameters are misleading. This paper proposes an outlier robust method based on linearization to estimate fixed effects parameters and variance components in the NLMM. An example is given using the 4-parameter logistic model and bioassay data, comparing the robust parameter estimates to the nonrobust estimates given by SASRĀ®.en
dc.format.extent21 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.sourceurlhttps://www.stat.vt.edu/content/dam/stat_vt_edu/graphics-and-pdfs/research-papers/Technical_Reports/TechReport14-1.pdfen
dc.identifier.urihttp://hdl.handle.net/10919/89419en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.ispartofseriesTechnical Report No. 14-1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectDose-responseen
dc.subjectLinearizationen
dc.subjectRobust Estimationen
dc.subjectm-estimationen
dc.titleOutlier Robust Nonlinear Mixed Model Estimationen
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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