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Outlier robust nonlinear mixed model estimation
Author(s) -
Williams James D.,
Birch Jeffrey B.,
AbdelSalam AbdelSalam G.
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6406
Subject(s) - outlier , nonlinear system , linearization , variance (accounting) , statistics , robust statistics , estimation theory , cluster (spacecraft) , mathematics , computer science , variance components , mixed model , anomaly detection , standard error , econometrics , data mining , physics , accounting , quantum mechanics , business , programming language
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 ® . Copyright © 2015 John Wiley & Sons, Ltd.