Simultaneously model-unbiased, design-unbiased estimation.
This paper proposes a class of inferential procedures (incorporating both design and estimation elements) that yield estimates of means that are simultaneously model unbiased and design unbiased. Classical regression procedures yield conditionally unbiased estimators for the mean (conditioning on the model and choice of observation points). In contrast, design-based methods yield estimators that are unconditionally unbiased on matter what the form of the underlying model. Variance properties of the proposed class are examined, and applications to bioavailability, water quality from mine run-off, and finite population regression estimation are considered. The proposed procedures perform well, especially in the typical case where a model is only approximately correct.
Gerow
K
K
Department of Statistics, University of Wyoming, Laramie 82071, USA. gerow@uwyo.edu
McCulloch
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CE
D000818:Animals; D019540:Area Under Curve; D001555:Benzene Derivatives / Q000493:pharmacokinetics; D015233:Models, Statistical; D012044:Regression Analysis; D015203:Reproducibility of Results; D012107:Research Design; D014018:Tissue Distribution; D014116:Toxicology / Q000379:methods
D001555:Benzene Derivatives