Abstract
In linear regression the mean surface is a plane in sample space; in non-linear regression it may be an arbitrary curved surface but in all other respects the models are the same. Fortunately the mean surface in most non-linear regression models met in practice will be approximately planar in the region of highest likelihood, allowing some good approximations based on linear regression to be used, but non-linear regression models can still present tricky computational and inferential problems.
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© 2002 Springer Science+Business Media New York
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Venables, W.N., Ripley, B.D. (2002). Non-Linear and Smooth Regression. In: Modern Applied Statistics with S. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21706-2_8
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DOI: https://doi.org/10.1007/978-0-387-21706-2_8
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-3008-8
Online ISBN: 978-0-387-21706-2
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