IngentaConnect Simultaneously Model-Unbiased, Design-Unbiased Estimation

Authors: Gerow, Ken¹; McCulloch, Charles E.²

Source: Biometrics, Volume 56, Number 3, September 2000 , pp. 873-878(6)

Abstract:

Summary.

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 no 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.

Keywords: Area under a curve; Design-based sampling; Environmental toxicology; Mean balanced; Model-based sampling; Regression estimator; Robust estimation

Document Type: Research article

DOI: 10.1111/j.0006-341X.2000.00873.x

Affiliations: 1: Departments of Statistics and Zoology & Physiology, P.O. Box 3332, University of Wyoming, Laramie, Wyoming 82071, U.S.A., Email: gerow@uwyo.edu 2: Biometrics Unit, Department of Statistical Science, Cornell University, 434 Warren Hall, Ithaca, New York 14853, U.S.A.

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