title: Outlier robust nonlinear mixed model estimation.
abstract: In standard analyses of data well-modeled by a nonlinear mixed model, 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 nonlinear mixed model. An example is given using the four-parameter logistic model and bioassay data, comparing the robust parameter estimates with the nonrobust estimates given by SAS(®).
authors.affiliation: Business Analytics, Dow AgroSciences, 9330 Zionsville Rd., Indianapolis, IN, 46268, U.S.A.
authors.forename: James D
authors.initials: JD
authors.lastname: Williams
authors.forename: Jeffrey B
authors.initials: JB
authors.lastname: Birch
authors.forename: Abdel-Salam G
authors.initials: AS
authors.lastname: Abdel-Salam
mesh_terms: D015982:Bias; D001681:Biological Assay / Q000379:methods / Q000706:statistics & numerical data; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics; D016011:Normal Distribution
keywords: M-estimation; dose-response; linearization; robust estimation