title: Sensitivity of parametric link functions in generalized linear models.
abstract: A common method of choosing the link function in generalized linear models is to specify a parametric link family indexed by unknown parameters. The maximum likelihood estimates of such link parameters, however, may often depend on one or several extreme observations. Diagnostics are derived to assess the sensitivity of the parametric link analysis. Two examples demonstrate that the proposed diagnostics can identify jointly influential observations on the link even when masking is present.
authors.affiliation: Faculty of Science, Northern Territory University, Darwin, Australia.
authors.forename: J S
authors.initials: JS
authors.lastname: Yick
authors.forename: A H
authors.initials: AH
authors.lastname: Lee
mesh_terms: D001799:Blood Sedimentation; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000175:diagnosis; D016014:Linear Models; D008432:Mathematical Computing; D008954:Models, Biological; D011237:Predictive Value of Tests; D011379:Prognosis; D012680:Sensitivity and Specificity