Journal article · 2015
Outlier robust nonlinear mixed model estimation
Statistics in Medicine · 34(8), 1304–1316 · 2015
Public health and biostatisticsRobust and semiparametric modelling
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 ® . Copyright © 2015 John Wiley & Sons, Ltd.
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Details
- Type
- Journal article
- Year
- 2015
- Journal
- Statistics in Medicine
- Volume
- 34
- Issue
- 8
- Pages
- 1304–1316
- Publisher
- Wiley
- DOI
- 10.1002/sim.6406
- Open access
- Unpaywall (repository)
Cite this work
James D. Williams, Jeffrey B. Birch, Abdel-Salam G. Abdel-Salam (2015). Outlier robust nonlinear mixed model estimation. Statistics in Medicine, 34(8), 1304–1316. https://doi.org/10.1002/sim.6406
@article{williams2015outlier,
author = {James D. Williams and Jeffrey B. Birch and Abdel-Salam G. Abdel-Salam},
title = {Outlier robust nonlinear mixed model estimation},
year = {2015},
journal = {Statistics in Medicine},
volume = {34},
number = {8},
pages = {1304--1316},
publisher = {Wiley},
doi = {10.1002/sim.6406}
}
TY - JOUR AU - James D. Williams AU - Jeffrey B. Birch AU - Abdel-Salam G. Abdel-Salam TI - Outlier robust nonlinear mixed model estimation PY - 2015 JO - Statistics in Medicine VL - 34 IS - 8 SP - 1304 EP - 1316 PB - Wiley DO - 10.1002/sim.6406 UR - https://doi.org/10.1002/sim.6406 ER -