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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer Science+Business Media New York
About this chapter
Cite this chapter
Venables, W.N., Ripley, B.D. (1999). Non-linear Models. In: Modern Applied Statistics with S-PLUS. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3121-7_8
Download citation
DOI: https://doi.org/10.1007/978-1-4757-3121-7_8
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-3123-1
Online ISBN: 978-1-4757-3121-7
eBook Packages: Springer Book Archive