Dose–Response Curve Estimation: A Semiparametric Mixture Approach
Summary In the estimation of a dose–response curve, parametric models are straightforward and efficient but subject to model misspecifications; nonparametric methods are robust but less efficient. As a compromise, we propose a semiparametric approach
that combines the advantages of parametric and nonparametric curve estimates. In a mixture form, our estimator takes a weighted average of the parametric and nonparametric curve estimates, in which a higher weight is assigned to the estimate with a better model fit. When the parametric model
assumption holds, the semiparametric curve estimate converges to the parametric estimate and thus achieves high efficiency; when the parametric model is misspecified, the semiparametric estimate converges to the nonparametric estimate and remains consistent. We also consider an adaptive weighting
scheme to allow the weight to vary according to the local fit of the models. We conduct extensive simulation studies to investigate the performance of the proposed methods and illustrate them with two real examples.
Document Type: Research Article
Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, U.S.A.
Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong
Publication date: 2011-12-01