Abstract
In linear regression the mean surface in sample space is a plane. In non-linear regression the mean surface may be an arbitrary curved surface but in other respects the models are similar. In practice the mean surface in most non-linear regression models will be approximately planar in the region(s) of high likelihood allowing good approximations based on linear regression techniques to be used. Non-linear regression models can still present tricky computational and inferential problems. (Indeed, the examples here exceeded the capacity of S-PLUS for Windows 3.1.)
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© 1994 Springer Science+Business Media New York
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Venables, W.N., Ripley, B.D. (1994). Non-linear Regression Models. In: Modern Applied Statistics with S-Plus. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-2819-1_9
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DOI: https://doi.org/10.1007/978-1-4899-2819-1_9
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4899-2821-4
Online ISBN: 978-1-4899-2819-1
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