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Robust ridge regression estimators for nonlinear models with applications to high throughput screening assay data
Author(s) -
Lim Changwon
Publication year - 2014
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.6391
Subject(s) - estimator , regression , nonlinear regression , ridge , computer science , statistics , null hypothesis , linear regression , standard error , regression analysis , nonlinear system , mathematics , econometrics , biology , paleontology , physics , quantum mechanics
Nonlinear regression is often used to evaluate the toxicity of a chemical or a drug by fitting data from a dose–response study. Toxicologists and pharmacologists may draw a conclusion about whether a chemical is toxic by testing the significance of the estimated parameters. However, sometimes the null hypothesis cannot be rejected even though the fit is quite good. One possible reason for such cases is that the estimated standard errors of the parameter estimates are extremely large. In this paper, we propose robust ridge regression estimation procedures for nonlinear models to solve this problem. The asymptotic properties of the proposed estimators are investigated; in particular, their mean squared errors are derived. The performances of the proposed estimators are compared with several standard estimators using simulation studies. The proposed methodology is also illustrated using high throughput screening assay data obtained from the National Toxicology Program. Copyright © 2014 John Wiley & Sons, Ltd.