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Statistical inference for calibration points in nonlinear mixed effects models

Statistical inference for calibration points in nonlinear mixed effects models

Journal of Agricultural Biological and Environmental Statistics 8(4): 455-468

ISSN/ISBN: 1085-7117

DOI: 10.1198/1085711032642

Many disciplines conduct studies in which the primary objectives depend on inference based on a nonlinear relationship between the treatment and response. In particular, interest often focuses on calibration-that is, estimating the best treatment level to achieve a particular result. Often, data for such calibration come from experiments with split-plots or other features that result in multiple error terms or other nontrivial error structures. One such example is the time-of-weed-removal study in weed science, designed to estimate the critical period of weed control. Calibration, or inverse prediction, is not a trivial problem with simple linear regression, and the complexities of experiments such as the time-of-weed-removal study further complicate the procedure. In this article, we extend existing calibration techniques to nonlinear mixed effects models, and illustrate the procedure using data from a time-of-weed-removal study.

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Accession: 012581919

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