Abstract
This paper points out that so-called optimal designs for nonlinear regression models are often limited when the assumed model function is not known with complete certainty and argues that robust designs - near optimal designs but with extra support points - can be used to also test for lack of fit of the model function. A simple robust design strategy - which has been implemented with a popular software package - is also presented and illustrated.
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© 1995 Springer Science+Business Media New York
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O’Brien, T.E. (1995). Optimal Design and Lack of Fit in Nonlinear Regression Models. In: Seeber, G.U.H., Francis, B.J., Hatzinger, R., Steckel-Berger, G. (eds) Statistical Modelling. Lecture Notes in Statistics, vol 104. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0789-4_25
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DOI: https://doi.org/10.1007/978-1-4612-0789-4_25
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94565-1
Online ISBN: 978-1-4612-0789-4
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